测试 minpy gpu加速 numpy 矩阵相乘 matmul matrix multiplication

版权声明：版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/DarrenXf/article/details/86102025

这篇文章的测试不准确,可能是minpy和numpy同时用出的问题,现在最新的测试在下面这篇文章中
https://blog.csdn.net/DarrenXf/article/details/86305215
因为觉得这是整个测试过程,就没有删除这篇文章.
测试minpy 调用gpu 加速numpy的矩阵相乘.

main.py

#!/usr/bin/python
# -*- coding: utf-8 -*-
#####################################
# File name : main.py
# Create date : 2019-01-05 17:11
# Modified date : 2019-01-08 18:01
# Author : DARREN
# Describe : not set
# Email : [email protected]
#####################################
from __future__ import division
from __future__ import print_function

import time
import numpy as np
import numpy.random as random
import minpy.numpy as mnp
import matplotlib.pyplot as plt

def _plot_record(record):
    _plot_a_key(record, "numpy", "minpy")
    _plot_key(record,"acceleration")

def _plot_a_key(record, name1, name2):
    numpy_lt = []
    minpy_lt = []
    steps = []
    for key in record:
        steps.append([key])
    steps.sort()

    for i in range(len(steps)):
        step_dic = record[steps[i][0]]
        numpy_value = step_dic[name1]
        numpy_lt.append(numpy_value)
        minpy_value = step_dic[name2]
        minpy_lt.append(minpy_value)

    numpy_lt = np.array(numpy_lt)
    minpy_lt = np.array(minpy_lt)
    steps = np.array(steps)
    numpy_line, = plt.plot(steps, numpy_lt)
    minpy_line, = plt.plot(steps, minpy_lt)
    plt.legend(handles=[numpy_line,minpy_line],labels=['use numpy','use minpy'],loc='best')
    full_path_name = "./%s_%s.jpg" % (name1, name2)
#    plt.show()
    plt.savefig(full_path_name)
    plt.close()

def _plot_key(record, name):
    acceleration_lt= []
    steps = []
    for key in record:
        steps.append([key])
    steps.sort()

    for i in range(len(steps)):
        step_dic = record[steps[i][0]]
        acceleration_value = step_dic[name]
        acceleration_lt.append(acceleration_value)

    acceleration_lt = np.array(acceleration_lt)
    steps = np.array(steps)
    acceleration_line, = plt.plot(steps, acceleration_lt)
    plt.legend(handles=[acceleration_line],labels=['acceleartion'],loc='best')
    full_path_name = "./%s.jpg" % (name)
#    plt.show()
    plt.savefig(full_path_name)
    plt.close()

def test_numpy(A,B,i):
    s = time.time()
    np.dot(A,B)
    e = time.time()
    take_time = e - s
    return take_time

def test_minpy(A,B,i):
    s = time.time()
    mnp.dot(A,B)
    e = time.time()
    take_time = e - s
    return take_time

def _write_status(file_obj, A, B, i, numpy_take_time, minpy_take_time):
    acceleration  =  numpy_take_time / minpy_take_time
    shape_str = "%s : %s matmul %s" % (A.dtype, A.shape, B.shape)
    numpy_str = "i:%s use numpy:%s" % (i, numpy_take_time)
    minpy_str = "i:%s use minpy:%s" % (i, minpy_take_time)
    acceleration_str = "acceleration:%s" % acceleration

    file_obj.write("%s\n" % shape_str)
    file_obj.write("%s\n" % numpy_str)
    file_obj.write("%s\n" % minpy_str)
    file_obj.write("%s\n" % acceleration_str)

    print(shape_str)
    print(numpy_str)
    print(minpy_str)
    print(acceleration_str)

def _record_status(record, i, numpy_take_time, minpy_take_time):
    dic = {}
    dic["numpy"] = numpy_take_time
    dic["minpy"] = minpy_take_time
    dic["acceleration"] =  numpy_take_time / minpy_take_time

    record[i] = dic

def test_numpy_and_minpy():
#    random.seed(0)
    file_obj = open("./output","w")
    record = {}
    for i in range(1,3000):
        A = random.randn(i, i)
        B = random.randn(i, i)
        numpy_take_time = test_numpy(A,B,i)
        minpy_take_time = test_minpy(A,B,i)
        _write_status(file_obj,A,B,i,numpy_take_time,minpy_take_time)
        _record_status(record, i, numpy_take_time, minpy_take_time)

    file_obj.close()
    _plot_record(record)

if __name__ == '__main__':
    test_numpy_and_minpy()

下面是我机器中的cpu和gpu型号

31.4 GiB
Intel® Core™ i7-8700K CPU @ 3.70GHz × 12 
GeForce GTX 1080 Ti/PCIe/SSE2
64-bit

randn 返回的是float64

测试都用的方阵
下面是1-3000的结果.
numpy minpy 求矩阵相乘的时间对比图

可以看出来从1000开始gpu和cpu之间就有了差距.
下面是1-3000的加速效果图
numpy_take_time / minpy_take_time

能看出gpu处理矩阵相乘,能够起到加速的效果.矩阵行数3000以内大概能加速六七倍. 输出太长放到下面.
3000-10000的结果.
numpy minpy 求矩阵相乘的时间对比图

然后3000-1000的加速效果图

6000的时候会掉下来不知道为啥,可以重复测试下,看是否一定会这样.

1-3000 output

float64 : (1, 1) matmul (1, 1)
i:1 use numpy:4.05311584473e-06
i:1 use minpy:0.000562906265259
acceleration:0.00720033883947
float64 : (2, 2) matmul (2, 2)
i:2 use numpy:1.62124633789e-05
i:2 use minpy:0.0029399394989
acceleration:0.00551455680804
float64 : (3, 3) matmul (3, 3)
i:3 use numpy:4.05311584473e-06
i:3 use minpy:0.000226974487305
acceleration:0.0178571428571
float64 : (4, 4) matmul (4, 4)
i:4 use numpy:5.00679016113e-06
i:4 use minpy:0.000178813934326
acceleration:0.028
float64 : (5, 5) matmul (5, 5)
i:5 use numpy:5.00679016113e-06
i:5 use minpy:0.000164985656738
acceleration:0.0303468208092
float64 : (6, 6) matmul (6, 6)
i:6 use numpy:5.00679016113e-06
i:6 use minpy:0.000138998031616
acceleration:0.0360205831904
float64 : (7, 7) matmul (7, 7)
i:7 use numpy:4.05311584473e-06
i:7 use minpy:0.000137090682983
acceleration:0.0295652173913
float64 : (8, 8) matmul (8, 8)
i:8 use numpy:5.00679016113e-06
i:8 use minpy:0.000160932540894
acceleration:0.0311111111111
float64 : (9, 9) matmul (9, 9)
i:9 use numpy:5.00679016113e-06
i:9 use minpy:0.000140905380249
acceleration:0.0355329949239
float64 : (10, 10) matmul (10, 10)
i:10 use numpy:4.05311584473e-06
i:10 use minpy:0.000133991241455
acceleration:0.0302491103203
float64 : (11, 11) matmul (11, 11)
i:11 use numpy:4.05311584473e-06
i:11 use minpy:0.000207901000977
acceleration:0.019495412844
float64 : (12, 12) matmul (12, 12)
i:12 use numpy:4.05311584473e-06
i:12 use minpy:0.000197172164917
acceleration:0.0205562273277
float64 : (13, 13) matmul (13, 13)
i:13 use numpy:3.09944152832e-06
i:13 use minpy:0.000149965286255
acceleration:0.0206677265501
float64 : (14, 14) matmul (14, 14)
i:14 use numpy:2.86102294922e-06
i:14 use minpy:0.000213861465454
acceleration:0.0133779264214
float64 : (15, 15) matmul (15, 15)
i:15 use numpy:4.05311584473e-06
i:15 use minpy:0.000186920166016
acceleration:0.0216836734694
float64 : (16, 16) matmul (16, 16)
i:16 use numpy:3.09944152832e-06
i:16 use minpy:0.000194072723389
acceleration:0.0159705159705
float64 : (17, 17) matmul (17, 17)
i:17 use numpy:2.86102294922e-06
i:17 use minpy:0.000167846679688
acceleration:0.0170454545455
float64 : (18, 18) matmul (18, 18)
i:18 use numpy:2.86102294922e-06
i:18 use minpy:0.000200986862183
acceleration:0.0142348754448
float64 : (19, 19) matmul (19, 19)
i:19 use numpy:1.90734863281e-05
i:19 use minpy:0.000169038772583
acceleration:0.112834978843
float64 : (20, 20) matmul (20, 20)
i:20 use numpy:1.69277191162e-05
i:20 use minpy:0.0001540184021
acceleration:0.109907120743
float64 : (21, 21) matmul (21, 21)
i:21 use numpy:1.81198120117e-05
i:21 use minpy:0.000185966491699
acceleration:0.0974358974359
float64 : (22, 22) matmul (22, 22)
i:22 use numpy:5.00679016113e-06
i:22 use minpy:0.000148057937622
acceleration:0.0338164251208
float64 : (23, 23) matmul (23, 23)
i:23 use numpy:7.15255737305e-06
i:23 use minpy:0.0001380443573
acceleration:0.0518134715026
float64 : (24, 24) matmul (24, 24)
i:24 use numpy:6.19888305664e-06
i:24 use minpy:0.000134944915771
acceleration:0.0459363957597
float64 : (25, 25) matmul (25, 25)
i:25 use numpy:3.81469726562e-06
i:25 use minpy:0.000137090682983
acceleration:0.0278260869565
float64 : (26, 26) matmul (26, 26)
i:26 use numpy:3.81469726562e-06
i:26 use minpy:0.000137090682983
acceleration:0.0278260869565
float64 : (27, 27) matmul (27, 27)
i:27 use numpy:5.96046447754e-06
i:27 use minpy:0.0001380443573
acceleration:0.0431778929188
float64 : (28, 28) matmul (28, 28)
i:28 use numpy:4.05311584473e-06
i:28 use minpy:0.000138998031616
acceleration:0.0291595197256
float64 : (29, 29) matmul (29, 29)
i:29 use numpy:5.00679016113e-06
i:29 use minpy:0.000134944915771
acceleration:0.0371024734982
float64 : (30, 30) matmul (30, 30)
i:30 use numpy:5.00679016113e-06
i:30 use minpy:0.000134944915771
acceleration:0.0371024734982
float64 : (31, 31) matmul (31, 31)
i:31 use numpy:5.96046447754e-06
i:31 use minpy:0.000133991241455
acceleration:0.0444839857651
float64 : (32, 32) matmul (32, 32)
i:32 use numpy:5.00679016113e-06
i:32 use minpy:0.000137090682983
acceleration:0.0365217391304
float64 : (33, 33) matmul (33, 33)
i:33 use numpy:6.91413879395e-06
i:33 use minpy:0.000135898590088
acceleration:0.0508771929825
float64 : (34, 34) matmul (34, 34)
i:34 use numpy:6.19888305664e-06
i:34 use minpy:0.0001380443573
acceleration:0.0449050086356
float64 : (35, 35) matmul (35, 35)
i:35 use numpy:7.15255737305e-06
i:35 use minpy:0.000136852264404
acceleration:0.0522648083624
float64 : (36, 36) matmul (36, 36)
i:36 use numpy:5.96046447754e-06
i:36 use minpy:0.000136852264404
acceleration:0.0435540069686
float64 : (37, 37) matmul (37, 37)
i:37 use numpy:7.15255737305e-06
i:37 use minpy:0.000153064727783
acceleration:0.0467289719626
float64 : (38, 38) matmul (38, 38)
i:38 use numpy:7.15255737305e-06
i:38 use minpy:0.000166893005371
acceleration:0.0428571428571
float64 : (39, 39) matmul (39, 39)
i:39 use numpy:1.00135803223e-05
i:39 use minpy:0.000187158584595
acceleration:0.0535031847134
float64 : (40, 40) matmul (40, 40)
i:40 use numpy:1.00135803223e-05
i:40 use minpy:0.000142097473145
acceleration:0.0704697986577
float64 : (41, 41) matmul (41, 41)
i:41 use numpy:8.10623168945e-06
i:41 use minpy:0.000141859054565
acceleration:0.0571428571429
float64 : (42, 42) matmul (42, 42)
i:42 use numpy:8.10623168945e-06
i:42 use minpy:0.000140905380249
acceleration:0.0575296108291
float64 : (43, 43) matmul (43, 43)
i:43 use numpy:1.09672546387e-05
i:43 use minpy:0.000176906585693
acceleration:0.0619946091644
float64 : (44, 44) matmul (44, 44)
i:44 use numpy:8.10623168945e-06
i:44 use minpy:0.000141859054565
acceleration:0.0571428571429
float64 : (45, 45) matmul (45, 45)
i:45 use numpy:1.00135803223e-05
i:45 use minpy:0.000141143798828
acceleration:0.0709459459459
float64 : (46, 46) matmul (46, 46)
i:46 use numpy:1.19209289551e-05
i:46 use minpy:0.000137805938721
acceleration:0.0865051903114
float64 : (47, 47) matmul (47, 47)
i:47 use numpy:1.09672546387e-05
i:47 use minpy:0.000140905380249
acceleration:0.077834179357
float64 : (48, 48) matmul (48, 48)
i:48 use numpy:1.00135803223e-05
i:48 use minpy:0.000142812728882
acceleration:0.0701168614357
float64 : (49, 49) matmul (49, 49)
i:49 use numpy:1.09672546387e-05
i:49 use minpy:0.000141143798828
acceleration:0.0777027027027
float64 : (50, 50) matmul (50, 50)
i:50 use numpy:1.09672546387e-05
i:50 use minpy:0.000144958496094
acceleration:0.0756578947368
float64 : (51, 51) matmul (51, 51)
i:51 use numpy:1.4066696167e-05
i:51 use minpy:0.000144958496094
acceleration:0.0970394736842
float64 : (52, 52) matmul (52, 52)
i:52 use numpy:1.21593475342e-05
i:52 use minpy:0.000141859054565
acceleration:0.0857142857143
float64 : (53, 53) matmul (53, 53)
i:53 use numpy:1.28746032715e-05
i:53 use minpy:0.000144004821777
acceleration:0.0894039735099
float64 : (54, 54) matmul (54, 54)
i:54 use numpy:1.28746032715e-05
i:54 use minpy:0.000144004821777
acceleration:0.0894039735099
float64 : (55, 55) matmul (55, 55)
i:55 use numpy:1.59740447998e-05
i:55 use minpy:0.000141859054565
acceleration:0.112605042017
float64 : (56, 56) matmul (56, 56)
i:56 use numpy:1.50203704834e-05
i:56 use minpy:0.000139951705933
acceleration:0.107325383305
float64 : (57, 57) matmul (57, 57)
i:57 use numpy:1.59740447998e-05
i:57 use minpy:0.000138998031616
acceleration:0.114922813036
float64 : (58, 58) matmul (58, 58)
i:58 use numpy:1.50203704834e-05
i:58 use minpy:0.000147104263306
acceleration:0.102106969206
float64 : (59, 59) matmul (59, 59)
i:59 use numpy:1.69277191162e-05
i:59 use minpy:0.000147819519043
acceleration:0.114516129032
float64 : (60, 60) matmul (60, 60)
i:60 use numpy:1.90734863281e-05
i:60 use minpy:0.000298976898193
acceleration:0.0637958532695
float64 : (61, 61) matmul (61, 61)
i:61 use numpy:2.40802764893e-05
i:61 use minpy:0.000262022018433
acceleration:0.0919017288444
float64 : (62, 62) matmul (62, 62)
i:62 use numpy:1.90734863281e-05
i:62 use minpy:0.000216960906982
acceleration:0.0879120879121
float64 : (63, 63) matmul (63, 63)
i:63 use numpy:2.00271606445e-05
i:63 use minpy:0.000174045562744
acceleration:0.115068493151
float64 : (64, 64) matmul (64, 64)
i:64 use numpy:1.81198120117e-05
i:64 use minpy:0.000151872634888
acceleration:0.119309262166
float64 : (65, 65) matmul (65, 65)
i:65 use numpy:0.00897216796875
i:65 use minpy:0.00435209274292
acceleration:2.06157554509
float64 : (66, 66) matmul (66, 66)
i:66 use numpy:0.0143010616302
i:66 use minpy:0.00830793380737
acceleration:1.7213740458
float64 : (67, 67) matmul (67, 67)
i:67 use numpy:0.0134129524231
i:67 use minpy:0.00657105445862
acceleration:2.04121766264
float64 : (68, 68) matmul (68, 68)
i:68 use numpy:0.00870203971863
i:68 use minpy:0.00947403907776
acceleration:0.918514231069
float64 : (69, 69) matmul (69, 69)
i:69 use numpy:0.00623083114624
i:69 use minpy:0.0107781887054
acceleration:0.578096312518
float64 : (70, 70) matmul (70, 70)
i:70 use numpy:0.00681805610657
i:70 use minpy:0.00875115394592
acceleration:0.779103664351
float64 : (71, 71) matmul (71, 71)
i:71 use numpy:0.0092887878418
i:71 use minpy:0.00873184204102
acceleration:1.06378331149
float64 : (72, 72) matmul (72, 72)
i:72 use numpy:0.0184721946716
i:72 use minpy:0.00949192047119
acceleration:1.94609665428
float64 : (73, 73) matmul (73, 73)
i:73 use numpy:0.00976300239563
i:73 use minpy:0.00849986076355
acceleration:1.14860733219
float64 : (74, 74) matmul (74, 74)
i:74 use numpy:0.00575113296509
i:74 use minpy:0.0080349445343
acceleration:0.715765110828
float64 : (75, 75) matmul (75, 75)
i:75 use numpy:0.0096230506897
i:75 use minpy:0.0108089447021
acceleration:0.890285865537
float64 : (76, 76) matmul (76, 76)
i:76 use numpy:0.00786209106445
i:76 use minpy:0.0112431049347
acceleration:0.699281124753
float64 : (77, 77) matmul (77, 77)
i:77 use numpy:0.0143451690674
i:77 use minpy:0.00811195373535
acceleration:1.76839877733
float64 : (78, 78) matmul (78, 78)
i:78 use numpy:0.0129401683807
i:78 use minpy:0.0108680725098
acceleration:1.19065900316
float64 : (79, 79) matmul (79, 79)
i:79 use numpy:0.00884985923767
i:79 use minpy:0.00945901870728
acceleration:0.93560014115
float64 : (80, 80) matmul (80, 80)
i:80 use numpy:0.0148930549622
i:80 use minpy:0.00908803939819
acceleration:1.63875334488
float64 : (81, 81) matmul (81, 81)
i:81 use numpy:0.0168950557709
i:81 use minpy:0.0107619762421
acceleration:1.56988413567
float64 : (82, 82) matmul (82, 82)
i:82 use numpy:0.0169451236725
i:82 use minpy:0.00797510147095
acceleration:2.12475336323
float64 : (83, 83) matmul (83, 83)
i:83 use numpy:0.00708794593811
i:83 use minpy:0.00671792030334
acceleration:1.05508038471
float64 : (84, 84) matmul (84, 84)
i:84 use numpy:0.00865197181702
i:84 use minpy:0.00933313369751
acceleration:0.927016808869
float64 : (85, 85) matmul (85, 85)
i:85 use numpy:0.0100629329681
i:85 use minpy:0.0107591152191
acceleration:0.935293726594
float64 : (86, 86) matmul (86, 86)
i:86 use numpy:0.0126070976257
i:86 use minpy:0.00853586196899
acceleration:1.4769565946
float64 : (87, 87) matmul (87, 87)
i:87 use numpy:0.01025390625
i:87 use minpy:0.0111811161041
acceleration:0.917073586797
float64 : (88, 88) matmul (88, 88)
i:88 use numpy:0.00878214836121
i:88 use minpy:0.0073869228363
acceleration:1.18887777168
float64 : (89, 89) matmul (89, 89)
i:89 use numpy:0.00852298736572
i:89 use minpy:0.00958895683289
acceleration:0.888833635844
float64 : (90, 90) matmul (90, 90)
i:90 use numpy:7.20024108887e-05
i:90 use minpy:0.000231027603149
acceleration:0.311661506708
float64 : (91, 91) matmul (91, 91)
i:91 use numpy:7.00950622559e-05
i:91 use minpy:0.000240087509155
acceleration:0.291956305859
float64 : (92, 92) matmul (92, 92)
i:92 use numpy:5.41210174561e-05
i:92 use minpy:0.000230073928833
acceleration:0.235233160622
float64 : (93, 93) matmul (93, 93)
i:93 use numpy:5.57899475098e-05
i:93 use minpy:0.000233173370361
acceleration:0.239263803681
float64 : (94, 94) matmul (94, 94)
i:94 use numpy:5.00679016113e-05
i:94 use minpy:0.000230073928833
acceleration:0.217616580311
float64 : (95, 95) matmul (95, 95)
i:95 use numpy:5.60283660889e-05
i:95 use minpy:0.000356912612915
acceleration:0.156980627923
float64 : (96, 96) matmul (96, 96)
i:96 use numpy:5.41210174561e-05
i:96 use minpy:0.000678062438965
acceleration:0.0798171589311
float64 : (97, 97) matmul (97, 97)
i:97 use numpy:6.19888305664e-05
i:97 use minpy:0.000236034393311
acceleration:0.262626262626
float64 : (98, 98) matmul (98, 98)
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float64 : (1011, 1011) matmul (1011, 1011)
i:1011 use numpy:0.0103600025177
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float64 : (1012, 1012) matmul (1012, 1012)
i:1012 use numpy:0.00770616531372
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acceleration:3.33870467927
float64 : (1013, 1013) matmul (1013, 1013)
i:1013 use numpy:0.0100598335266
i:1013 use minpy:0.00516200065613
acceleration:1.94882453466
float64 : (1014, 1014) matmul (1014, 1014)
i:1014 use numpy:0.0103571414948
i:1014 use minpy:0.00338196754456
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float64 : (1015, 1015) matmul (1015, 1015)
i:1015 use numpy:0.00898814201355
i:1015 use minpy:0.00254702568054
acceleration:3.52887765609
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i:1016 use numpy:0.0100519657135
i:1016 use minpy:0.00509786605835
acceleration:1.97179870919
float64 : (1017, 1017) matmul (1017, 1017)
i:1017 use numpy:0.0124361515045
i:1017 use minpy:0.00335597991943
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float64 : (1018, 1018) matmul (1018, 1018)
i:1018 use numpy:0.00811100006104
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float64 : (1019, 1019) matmul (1019, 1019)
i:1019 use numpy:0.0119690895081
i:1019 use minpy:0.00522208213806
acceleration:2.29201479249
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i:1020 use numpy:0.0101840496063
i:1020 use minpy:0.00382995605469
acceleration:2.65905129482
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i:1021 use numpy:0.0140950679779
i:1021 use minpy:0.00253510475159
acceleration:5.55995485752
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i:1022 use numpy:0.0126399993896
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i:1023 use numpy:0.0118250846863
i:1023 use minpy:0.00336813926697
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float64 : (1024, 1024) matmul (1024, 1024)
i:1024 use numpy:0.00822305679321
i:1024 use minpy:0.00234007835388
acceleration:3.51400916964
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i:1025 use numpy:0.011293888092
i:1025 use minpy:0.00528883934021
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i:1026 use numpy:0.0105249881744
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float64 : (1027, 1027) matmul (1027, 1027)
i:1027 use numpy:0.00996708869934
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i:1028 use numpy:0.0103869438171
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i:1029 use numpy:0.0106379985809
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i:1030 use numpy:0.0101139545441
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i:1031 use numpy:0.0105838775635
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i:1032 use numpy:0.01145195961
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i:1033 use numpy:0.010055065155
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i:1034 use numpy:0.0107040405273
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i:1035 use numpy:0.0111029148102
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i:1036 use numpy:0.00968599319458
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i:1037 use numpy:0.0123069286346
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i:1038 use numpy:0.0119590759277
i:1038 use minpy:0.00330996513367
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i:1039 use numpy:0.00865006446838
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i:1040 use numpy:0.0126600265503
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i:1041 use numpy:0.0121238231659
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i:1042 use numpy:0.0105528831482
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i:1043 use numpy:0.0109779834747
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i:1044 use numpy:0.0112311840057
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i:1045 use numpy:0.0103199481964
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i:1046 use numpy:0.011992931366
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i:1047 use numpy:0.014955997467
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i:1048 use numpy:0.00937795639038
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i:1049 use numpy:0.0108721256256
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float64 : (1050, 1050) matmul (1050, 1050)
i:1050 use numpy:0.0107080936432
i:1050 use minpy:0.00347399711609
acceleration:3.08235536339
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i:1051 use numpy:0.0103409290314
i:1051 use minpy:0.00239896774292
acceleration:4.31057443848
float64 : (1052, 1052) matmul (1052, 1052)
i:1052 use numpy:0.0114648342133
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i:1053 use numpy:0.0251529216766
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float64 : (1054, 1054) matmul (1054, 1054)
i:1054 use numpy:0.00852799415588
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float64 : (1055, 1055) matmul (1055, 1055)
i:1055 use numpy:0.0112581253052
i:1055 use minpy:0.0056529045105
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float64 : (1056, 1056) matmul (1056, 1056)
i:1056 use numpy:0.0105299949646
i:1056 use minpy:0.00337791442871
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i:1057 use numpy:0.0101408958435
i:1057 use minpy:0.00239205360413
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float64 : (1058, 1058) matmul (1058, 1058)
i:1058 use numpy:0.0124151706696
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i:1059 use numpy:0.0118360519409
i:1059 use minpy:0.00339603424072
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i:1060 use numpy:0.0093719959259
i:1060 use minpy:0.00244402885437
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i:1061 use numpy:0.0122931003571
i:1061 use minpy:0.00559616088867
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i:1062 use numpy:0.0116968154907
i:1062 use minpy:0.00340509414673
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i:1063 use numpy:0.0148630142212
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i:1064 use numpy:0.0135350227356
i:1064 use minpy:0.0174219608307
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float64 : (1065, 1065) matmul (1065, 1065)
i:1065 use numpy:0.0130398273468
i:1065 use minpy:0.015007019043
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i:1066 use numpy:0.0115449428558
i:1066 use minpy:0.0159649848938
acceleration:0.723141483229
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i:1067 use numpy:0.0127069950104
i:1067 use minpy:0.0151619911194
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i:1068 use numpy:0.0138020515442
i:1068 use minpy:0.0172929763794
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i:1069 use numpy:0.0121419429779
i:1069 use minpy:0.0159859657288
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float64 : (1070, 1070) matmul (1070, 1070)
i:1070 use numpy:0.011922121048
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i:1071 use numpy:0.0121700763702
i:1071 use minpy:0.0161049365997
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i:1072 use numpy:0.0119340419769
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i:1073 use numpy:0.0136590003967
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i:1074 use numpy:0.0149869918823
i:1074 use minpy:0.016163110733
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i:1075 use numpy:0.0137431621552
i:1075 use minpy:0.0158700942993
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i:1076 use numpy:0.0121819972992
i:1076 use minpy:0.016242980957
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i:1077 use numpy:0.0116012096405
i:1077 use minpy:0.0155770778656
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i:1078 use numpy:0.0120830535889
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i:1079 use numpy:0.0121829509735
i:1079 use minpy:0.0164468288422
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i:1080 use numpy:0.0314230918884
i:1080 use minpy:0.0161030292511
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i:1081 use numpy:0.0117921829224
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i:1082 use numpy:0.0141139030457
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i:1083 use numpy:0.0129129886627
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i:1084 use numpy:0.0125880241394
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i:1085 use numpy:0.0122230052948
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i:1086 use numpy:0.0126149654388
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i:1087 use numpy:0.0137281417847
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i:1088 use numpy:0.0144619941711
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i:1089 use numpy:0.0157868862152
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i:1090 use numpy:0.0154249668121
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i:1091 use numpy:0.0150351524353
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i:1092 use numpy:0.012992143631
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i:1093 use numpy:0.0151660442352
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i:1094 use numpy:0.0142550468445
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i:1095 use numpy:0.0146489143372
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i:1096 use numpy:0.0147769451141
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i:1097 use numpy:0.0146751403809
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i:1098 use numpy:0.0133121013641
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i:1099 use numpy:0.0294280052185
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i:1100 use numpy:0.0107719898224
i:1100 use minpy:0.0160930156708
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i:1101 use numpy:0.0239322185516
i:1101 use minpy:0.0195140838623
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i:1102 use numpy:0.01509308815
i:1102 use minpy:0.0166158676147
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i:1103 use numpy:0.0125799179077
i:1103 use minpy:0.0174970626831
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i:1104 use numpy:0.00989603996277
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i:1105 use numpy:0.0158979892731
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i:1106 use numpy:0.0131061077118
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i:1107 use numpy:0.0131649971008
i:1107 use minpy:0.0178039073944
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i:1108 use numpy:0.0126891136169
i:1108 use minpy:0.0164768695831
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i:1109 use numpy:0.0103199481964
i:1109 use minpy:0.0148260593414
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i:1110 use numpy:0.0127620697021
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i:1111 use numpy:0.012866973877
i:1111 use minpy:0.0176270008087
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i:1112 use numpy:0.0127761363983
i:1112 use minpy:0.0165390968323
acceleration:0.772480899524
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i:1113 use numpy:0.0148210525513
i:1113 use minpy:0.0192291736603
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i:1114 use numpy:0.0599789619446
i:1114 use minpy:0.0196089744568
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i:1115 use numpy:0.0130288600922
i:1115 use minpy:0.0184941291809
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i:1116 use numpy:0.0132350921631
i:1116 use minpy:0.0197720527649
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i:1117 use numpy:0.0132040977478
i:1117 use minpy:0.0185129642487
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i:1118 use numpy:0.0383100509644
i:1118 use minpy:0.0272619724274
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i:1119 use numpy:0.0109550952911
i:1119 use minpy:0.0372278690338
acceleration:0.294271350347
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i:1120 use numpy:0.0138070583344
i:1120 use minpy:0.0373048782349
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float64 : (1121, 1121) matmul (1121, 1121)
i:1121 use numpy:0.0134649276733
i:1121 use minpy:0.0459399223328
acceleration:0.293098616402
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i:1122 use numpy:0.0133590698242
i:1122 use minpy:0.0288109779358
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i:1123 use numpy:0.0133879184723
i:1123 use minpy:0.00477004051208
acceleration:2.80666766632
float64 : (1124, 1124) matmul (1124, 1124)
i:1124 use numpy:0.0138411521912
i:1124 use minpy:0.00625395774841
acceleration:2.21318287522
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i:1125 use numpy:0.0243937969208
i:1125 use minpy:0.0207450389862
acceleration:1.17588580754
float64 : (1126, 1126) matmul (1126, 1126)
i:1126 use numpy:0.0135879516602
i:1126 use minpy:0.0149168968201
acceleration:0.910910078957
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i:1127 use numpy:0.0109980106354
i:1127 use minpy:0.0138230323792
acceleration:0.79562937666
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i:1128 use numpy:0.0136959552765
i:1128 use minpy:0.0057680606842
acceleration:2.37444715414
float64 : (1129, 1129) matmul (1129, 1129)
i:1129 use numpy:0.0506899356842
i:1129 use minpy:0.00644493103027
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i:1130 use numpy:0.0209929943085
i:1130 use minpy:0.00486183166504
acceleration:4.31791879168
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i:1131 use numpy:0.0224709510803
i:1131 use minpy:0.00630617141724
acceleration:3.56332703214
float64 : (1132, 1132) matmul (1132, 1132)
i:1132 use numpy:0.0137181282043
i:1132 use minpy:0.00529599189758
acceleration:2.59028496826
float64 : (1133, 1133) matmul (1133, 1133)
i:1133 use numpy:0.0137360095978
i:1133 use minpy:0.00387692451477
acceleration:3.54301703462
float64 : (1134, 1134) matmul (1134, 1134)
i:1134 use numpy:0.016126871109
i:1134 use minpy:0.00281190872192
acceleration:5.73520434119
float64 : (1135, 1135) matmul (1135, 1135)
i:1135 use numpy:0.0147819519043
i:1135 use minpy:0.0222239494324
acceleration:0.665136138348
float64 : (1136, 1136) matmul (1136, 1136)
i:1136 use numpy:0.0170450210571
i:1136 use minpy:0.0173330307007
acceleration:0.983383768913
float64 : (1137, 1137) matmul (1137, 1137)
i:1137 use numpy:0.0184419155121
i:1137 use minpy:0.0167949199677
acceleration:1.09806510228
float64 : (1138, 1138) matmul (1138, 1138)
i:1138 use numpy:0.015575170517
i:1138 use minpy:0.0204710960388
acceleration:0.760837157299
float64 : (1139, 1139) matmul (1139, 1139)
i:1139 use numpy:0.0137979984283
i:1139 use minpy:0.0186221599579
acceleration:0.740945113754
float64 : (1140, 1140) matmul (1140, 1140)
i:1140 use numpy:0.0111780166626
i:1140 use minpy:0.0182240009308
acceleration:0.613367871581
float64 : (1141, 1141) matmul (1141, 1141)
i:1141 use numpy:0.0136580467224
i:1141 use minpy:0.0193259716034
acceleration:0.706719796691
float64 : (1142, 1142) matmul (1142, 1142)
i:1142 use numpy:0.0137588977814
i:1142 use minpy:0.0310349464417
acceleration:0.443335638012
float64 : (1143, 1143) matmul (1143, 1143)
i:1143 use numpy:0.0115301609039
i:1143 use minpy:0.0156619548798
acceleration:0.736189127887
float64 : (1144, 1144) matmul (1144, 1144)
i:1144 use numpy:0.0136768817902
i:1144 use minpy:0.0209078788757
acceleration:0.654149656761
float64 : (1145, 1145) matmul (1145, 1145)
i:1145 use numpy:0.0140080451965
i:1145 use minpy:0.00493192672729
acceleration:2.84027844919
float64 : (1146, 1146) matmul (1146, 1146)
i:1146 use numpy:0.0137510299683
i:1146 use minpy:0.00393891334534
acceleration:3.49107196901
float64 : (1147, 1147) matmul (1147, 1147)
i:1147 use numpy:0.0202119350433
i:1147 use minpy:0.00293803215027
acceleration:6.87941248073
float64 : (1148, 1148) matmul (1148, 1148)
i:1148 use numpy:0.043662071228
i:1148 use minpy:0.00665998458862
acceleration:6.5558817212
float64 : (1149, 1149) matmul (1149, 1149)
i:1149 use numpy:0.0149159431458
i:1149 use minpy:0.00392889976501
acceleration:3.7964682323
float64 : (1150, 1150) matmul (1150, 1150)
i:1150 use numpy:0.0164310932159
i:1150 use minpy:0.030709028244
acceleration:0.535057413259
float64 : (1151, 1151) matmul (1151, 1151)
i:1151 use numpy:0.0163512229919
i:1151 use minpy:0.00688409805298
acceleration:2.37521645771
float64 : (1152, 1152) matmul (1152, 1152)
i:1152 use numpy:0.036878824234
i:1152 use minpy:0.0044059753418
acceleration:8.37018398268
float64 : (1153, 1153) matmul (1153, 1153)
i:1153 use numpy:0.0107131004333
i:1153 use minpy:0.00296592712402
acceleration:3.61205787781
float64 : (1154, 1154) matmul (1154, 1154)
i:1154 use numpy:0.0140759944916
i:1154 use minpy:0.00651001930237
acceleration:2.16220472441
float64 : (1155, 1155) matmul (1155, 1155)
i:1155 use numpy:0.0138511657715
i:1155 use minpy:0.0040078163147
acceleration:3.45603807258
float64 : (1156, 1156) matmul (1156, 1156)
i:1156 use numpy:0.0108408927917
i:1156 use minpy:0.00286793708801
acceleration:3.78003159032
float64 : (1157, 1157) matmul (1157, 1157)
i:1157 use numpy:0.0135090351105
i:1157 use minpy:0.00652194023132
acceleration:2.07132151343
float64 : (1158, 1158) matmul (1158, 1158)
i:1158 use numpy:0.0137100219727
i:1158 use minpy:0.0159389972687
acceleration:0.860155864359
float64 : (1159, 1159) matmul (1159, 1159)
i:1159 use numpy:0.0109782218933
i:1159 use minpy:0.0150170326233
acceleration:0.731051344743
float64 : (1160, 1160) matmul (1160, 1160)
i:1160 use numpy:0.0138230323792
i:1160 use minpy:0.0186078548431
acceleration:0.742860071495
float64 : (1161, 1161) matmul (1161, 1161)
i:1161 use numpy:0.0208389759064
i:1161 use minpy:0.0282511711121
acceleration:0.737632285179
float64 : (1162, 1162) matmul (1162, 1162)
i:1162 use numpy:0.0139939785004
i:1162 use minpy:0.0400371551514
acceleration:0.349524796341
float64 : (1163, 1163) matmul (1163, 1163)
i:1163 use numpy:0.0138919353485
i:1163 use minpy:0.0410850048065
acceleration:0.338126657498
float64 : (1164, 1164) matmul (1164, 1164)
i:1164 use numpy:0.0146839618683
i:1164 use minpy:0.0416040420532
acceleration:0.352945558739
float64 : (1165, 1165) matmul (1165, 1165)
i:1165 use numpy:0.0140872001648
i:1165 use minpy:0.0512001514435
acceleration:0.275139814388
float64 : (1166, 1166) matmul (1166, 1166)
i:1166 use numpy:0.0143060684204
i:1166 use minpy:0.0220608711243
acceleration:0.648481573544
float64 : (1167, 1167) matmul (1167, 1167)
i:1167 use numpy:0.0142240524292
i:1167 use minpy:0.00510406494141
acceleration:2.78680866966
float64 : (1168, 1168) matmul (1168, 1168)
i:1168 use numpy:0.0142269134521
i:1168 use minpy:0.00671005249023
acceleration:2.12023877203
float64 : (1169, 1169) matmul (1169, 1169)
i:1169 use numpy:0.0190858840942
i:1169 use minpy:0.0051109790802
acceleration:3.7342911788
float64 : (1170, 1170) matmul (1170, 1170)
i:1170 use numpy:0.0143859386444
i:1170 use minpy:0.00407290458679
acceleration:3.53210794357
float64 : (1171, 1171) matmul (1171, 1171)
i:1171 use numpy:0.011579990387
i:1171 use minpy:0.0031361579895
acceleration:3.69241295423
float64 : (1172, 1172) matmul (1172, 1172)
i:1172 use numpy:0.0143980979919
i:1172 use minpy:0.00682306289673
acceleration:2.11021035712
float64 : (1173, 1173) matmul (1173, 1173)
i:1173 use numpy:0.0142869949341
i:1173 use minpy:0.00410199165344
acceleration:3.48294100552
float64 : (1174, 1174) matmul (1174, 1174)
i:1174 use numpy:0.0116589069366
i:1174 use minpy:0.0029149055481
acceleration:3.9997546213
float64 : (1175, 1175) matmul (1175, 1175)
i:1175 use numpy:0.0145270824432
i:1175 use minpy:0.00682497024536
acceleration:2.1285195277
float64 : (1176, 1176) matmul (1176, 1176)
i:1176 use numpy:0.0223491191864
i:1176 use minpy:0.00445580482483
acceleration:5.01573117877
float64 : (1177, 1177) matmul (1177, 1177)
i:1177 use numpy:0.0119049549103
i:1177 use minpy:0.00293493270874
acceleration:4.05629569456
float64 : (1178, 1178) matmul (1178, 1178)
i:1178 use numpy:0.0147449970245
i:1178 use minpy:0.00682306289673
acceleration:2.16105248445
float64 : (1179, 1179) matmul (1179, 1179)
i:1179 use numpy:0.0147130489349
i:1179 use minpy:0.00411486625671
acceleration:3.5755837534
float64 : (1180, 1180) matmul (1180, 1180)
i:1180 use numpy:0.0119481086731
i:1180 use minpy:0.00305986404419
acceleration:3.90478416706
float64 : (1181, 1181) matmul (1181, 1181)
i:1181 use numpy:0.0148010253906
i:1181 use minpy:0.00700402259827
acceleration:2.11321782347
float64 : (1182, 1182) matmul (1182, 1182)
i:1182 use numpy:0.0148730278015
i:1182 use minpy:0.00425696372986
acceleration:3.49381125735
float64 : (1183, 1183) matmul (1183, 1183)
i:1183 use numpy:0.012148141861
i:1183 use minpy:0.00295805931091
acceleration:4.10679455146
float64 : (1184, 1184) matmul (1184, 1184)
i:1184 use numpy:0.015074968338
i:1184 use minpy:0.00704121589661
acceleration:2.14096095893
float64 : (1185, 1185) matmul (1185, 1185)
i:1185 use numpy:0.0150229930878
i:1185 use minpy:0.00417304039001
acceleration:3.60001142661
float64 : (1186, 1186) matmul (1186, 1186)
i:1186 use numpy:0.0124077796936
i:1186 use minpy:0.00297999382019
acceleration:4.16369309545
float64 : (1187, 1187) matmul (1187, 1187)
i:1187 use numpy:0.0152950286865
i:1187 use minpy:0.00696396827698
acceleration:2.19630935671
float64 : (1188, 1188) matmul (1188, 1188)
i:1188 use numpy:0.0169551372528
i:1188 use minpy:0.0041868686676
acceleration:4.04959854222
float64 : (1189, 1189) matmul (1189, 1189)
i:1189 use numpy:0.0124399662018
i:1189 use minpy:0.00299692153931
acceleration:4.15091487669
float64 : (1190, 1190) matmul (1190, 1190)
i:1190 use numpy:0.0162088871002
i:1190 use minpy:0.00697898864746
acceleration:2.32252664662
float64 : (1191, 1191) matmul (1191, 1191)
i:1191 use numpy:0.0152478218079
i:1191 use minpy:0.0435891151428
acceleration:0.349808014177
float64 : (1192, 1192) matmul (1192, 1192)
i:1192 use numpy:0.0124049186707
i:1192 use minpy:0.0162310600281
acceleration:0.764270395723
float64 : (1193, 1193) matmul (1193, 1193)
i:1193 use numpy:0.0153620243073
i:1193 use minpy:0.0227320194244
acceleration:0.675787928051
float64 : (1194, 1194) matmul (1194, 1194)
i:1194 use numpy:0.0153667926788
i:1194 use minpy:0.0200140476227
acceleration:0.767800345464
float64 : (1195, 1195) matmul (1195, 1195)
i:1195 use numpy:0.0155498981476
i:1195 use minpy:0.0210349559784
acceleration:0.739240821971
float64 : (1196, 1196) matmul (1196, 1196)
i:1196 use numpy:0.0172100067139
i:1196 use minpy:0.0191419124603
acceleration:0.89907456998
float64 : (1197, 1197) matmul (1197, 1197)
i:1197 use numpy:0.0152721405029
i:1197 use minpy:0.0212438106537
acceleration:0.718898353591
float64 : (1198, 1198) matmul (1198, 1198)
i:1198 use numpy:0.0152640342712
i:1198 use minpy:0.0202209949493
acceleration:0.754860693526
float64 : (1199, 1199) matmul (1199, 1199)
i:1199 use numpy:0.0162370204926
i:1199 use minpy:0.0523900985718
acceleration:0.309925366342
float64 : (1200, 1200) matmul (1200, 1200)
i:1200 use numpy:0.0124089717865
i:1200 use minpy:0.0487670898438
acceleration:0.254453809449
float64 : (1201, 1201) matmul (1201, 1201)
i:1201 use numpy:0.015547990799
i:1201 use minpy:0.0519669055939
acceleration:0.299190236965
float64 : (1202, 1202) matmul (1202, 1202)
i:1202 use numpy:0.0155789852142
i:1202 use minpy:0.050192117691
acceleration:0.310387087274
float64 : (1203, 1203) matmul (1203, 1203)
i:1203 use numpy:0.0126230716705
i:1203 use minpy:0.0174179077148
acceleration:0.724718024529
float64 : (1204, 1204) matmul (1204, 1204)
i:1204 use numpy:0.0158081054688
i:1204 use minpy:0.0199391841888
acceleration:0.79281606103
float64 : (1205, 1205) matmul (1205, 1205)
i:1205 use numpy:0.0156149864197
i:1205 use minpy:0.0204789638519
acceleration:0.762489085511
float64 : (1206, 1206) matmul (1206, 1206)
i:1206 use numpy:0.0135951042175
i:1206 use minpy:0.0159211158752
acceleration:0.853903980353
float64 : (1207, 1207) matmul (1207, 1207)
i:1207 use numpy:0.0158758163452
i:1207 use minpy:0.0235459804535
acceleration:0.674247410363
float64 : (1208, 1208) matmul (1208, 1208)
i:1208 use numpy:0.0159249305725
i:1208 use minpy:0.0215389728546
acceleration:0.739354224549
float64 : (1209, 1209) matmul (1209, 1209)
i:1209 use numpy:0.0161089897156
i:1209 use minpy:0.0206429958344
acceleration:0.780361040851
float64 : (1210, 1210) matmul (1210, 1210)
i:1210 use numpy:0.0158529281616
i:1210 use minpy:0.0179741382599
acceleration:0.881985435541
float64 : (1211, 1211) matmul (1211, 1211)
i:1211 use numpy:0.0130620002747
i:1211 use minpy:0.0185530185699
acceleration:0.704036393076
float64 : (1212, 1212) matmul (1212, 1212)
i:1212 use numpy:0.0162110328674
i:1212 use minpy:0.0217990875244
acceleration:0.743656487882
float64 : (1213, 1213) matmul (1213, 1213)
i:1213 use numpy:0.0161769390106
i:1213 use minpy:0.0208389759064
acceleration:0.776282821349
float64 : (1214, 1214) matmul (1214, 1214)
i:1214 use numpy:0.0254211425781
i:1214 use minpy:0.0202260017395
acceleration:1.25685456303
float64 : (1215, 1215) matmul (1215, 1215)
i:1215 use numpy:0.013347864151
i:1215 use minpy:0.0486481189728
acceleration:0.274375750447
float64 : (1216, 1216) matmul (1216, 1216)
i:1216 use numpy:0.0165848731995
i:1216 use minpy:0.0180230140686
acceleration:0.9202053073
float64 : (1217, 1217) matmul (1217, 1217)
i:1217 use numpy:0.0163497924805
i:1217 use minpy:0.01935505867
acceleration:0.844729678127
float64 : (1218, 1218) matmul (1218, 1218)
i:1218 use numpy:0.0133988857269
i:1218 use minpy:0.0169460773468
acceleration:0.790677715717
float64 : (1219, 1219) matmul (1219, 1219)
i:1219 use numpy:0.0164670944214
i:1219 use minpy:0.00730586051941
acceleration:2.25395685801
float64 : (1220, 1220) matmul (1220, 1220)
i:1220 use numpy:0.0163741111755
i:1220 use minpy:0.00453782081604
acceleration:3.60836441969
float64 : (1221, 1221) matmul (1221, 1221)
i:1221 use numpy:0.013561964035
i:1221 use minpy:0.00313401222229
acceleration:4.32734880183
float64 : (1222, 1222) matmul (1222, 1222)
i:1222 use numpy:0.0181019306183
i:1222 use minpy:0.00730013847351
acceleration:2.47966948627
float64 : (1223, 1223) matmul (1223, 1223)
i:1223 use numpy:0.0166349411011
i:1223 use minpy:0.0045599937439
acceleration:3.64801840427
float64 : (1224, 1224) matmul (1224, 1224)
i:1224 use numpy:0.0143299102783
i:1224 use minpy:0.0169241428375
acceleration:0.846714094527
float64 : (1225, 1225) matmul (1225, 1225)
i:1225 use numpy:0.017452955246
i:1225 use minpy:0.0196828842163
acceleration:0.886707204806
float64 : (1226, 1226) matmul (1226, 1226)
i:1226 use numpy:0.0170619487762
i:1226 use minpy:0.0195989608765
acceleration:0.870553744343
float64 : (1227, 1227) matmul (1227, 1227)
i:1227 use numpy:0.0169749259949
i:1227 use minpy:0.0191650390625
acceleration:0.88572352707
float64 : (1228, 1228) matmul (1228, 1228)
i:1228 use numpy:0.0168890953064
i:1228 use minpy:0.019632101059
acceleration:0.860279562319
float64 : (1229, 1229) matmul (1229, 1229)
i:1229 use numpy:0.0137419700623
i:1229 use minpy:0.0174059867859
acceleration:0.789496753691
float64 : (1230, 1230) matmul (1230, 1230)
i:1230 use numpy:0.0170538425446
i:1230 use minpy:0.020280122757
acceleration:0.84091416748
float64 : (1231, 1231) matmul (1231, 1231)
i:1231 use numpy:0.0170319080353
i:1231 use minpy:0.00744295120239
acceleration:2.2883272471
float64 : (1232, 1232) matmul (1232, 1232)
i:1232 use numpy:0.0168581008911
i:1232 use minpy:0.00571489334106
acceleration:2.94985398415
float64 : (1233, 1233) matmul (1233, 1233)
i:1233 use numpy:0.0168180465698
i:1233 use minpy:0.00449705123901
acceleration:3.73979429541
float64 : (1234, 1234) matmul (1234, 1234)
i:1234 use numpy:0.0139021873474
i:1234 use minpy:0.00331997871399
acceleration:4.18743267504
float64 : (1235, 1235) matmul (1235, 1235)
i:1235 use numpy:0.0171661376953
i:1235 use minpy:0.00748610496521
acceleration:2.29306665817
float64 : (1236, 1236) matmul (1236, 1236)
i:1236 use numpy:0.0170419216156
i:1236 use minpy:0.00450396537781
acceleration:3.78375946218
float64 : (1237, 1237) matmul (1237, 1237)
i:1237 use numpy:0.0139751434326
i:1237 use minpy:0.00321507453918
acceleration:4.34675565443
float64 : (1238, 1238) matmul (1238, 1238)
i:1238 use numpy:0.0176830291748
i:1238 use minpy:0.007483959198
acceleration:2.36279069767
float64 : (1239, 1239) matmul (1239, 1239)
i:1239 use numpy:0.0263669490814
i:1239 use minpy:0.00452089309692
acceleration:5.83224343424
float64 : (1240, 1240) matmul (1240, 1240)
i:1240 use numpy:0.0137801170349
i:1240 use minpy:0.00337600708008
acceleration:4.08177966102
float64 : (1241, 1241) matmul (1241, 1241)
i:1241 use numpy:0.016891002655
i:1241 use minpy:0.0075900554657
acceleration:2.22541228208
float64 : (1242, 1242) matmul (1242, 1242)
i:1242 use numpy:0.0167720317841
i:1242 use minpy:0.00456213951111
acceleration:3.67635223413
float64 : (1243, 1243) matmul (1243, 1243)
i:1243 use numpy:0.0138618946075
i:1243 use minpy:0.00325012207031
acceleration:4.26503814554
float64 : (1244, 1244) matmul (1244, 1244)
i:1244 use numpy:0.0169470310211
i:1244 use minpy:0.00770306587219
acceleration:2.20003714135
float64 : (1245, 1245) matmul (1245, 1245)
i:1245 use numpy:0.017333984375
i:1245 use minpy:0.00458288192749
acceleration:3.78233274373
float64 : (1246, 1246) matmul (1246, 1246)
i:1246 use numpy:0.0140569210052
i:1246 use minpy:0.00327515602112
acceleration:4.2919851496
float64 : (1247, 1247) matmul (1247, 1247)
i:1247 use numpy:0.0167970657349
i:1247 use minpy:0.00778293609619
acceleration:2.15819139811
float64 : (1248, 1248) matmul (1248, 1248)
i:1248 use numpy:0.0166211128235
i:1248 use minpy:0.00459814071655
acceleration:3.6147464482
float64 : (1249, 1249) matmul (1249, 1249)
i:1249 use numpy:0.013710975647
i:1249 use minpy:0.00349187850952
acceleration:3.92653284173
float64 : (1250, 1250) matmul (1250, 1250)
i:1250 use numpy:0.0168390274048
i:1250 use minpy:0.00760102272034
acceleration:2.21536338258
float64 : (1251, 1251) matmul (1251, 1251)
i:1251 use numpy:0.0260510444641
i:1251 use minpy:0.00490307807922
acceleration:5.3132020423
float64 : (1252, 1252) matmul (1252, 1252)
i:1252 use numpy:0.0137259960175
i:1252 use minpy:0.00327301025391
acceleration:4.19369172494
float64 : (1253, 1253) matmul (1253, 1253)
i:1253 use numpy:0.0181019306183
i:1253 use minpy:0.00800108909607
acceleration:2.26243332638
float64 : (1254, 1254) matmul (1254, 1254)
i:1254 use numpy:0.0169560909271
i:1254 use minpy:0.00463795661926
acceleration:3.65593995785
float64 : (1255, 1255) matmul (1255, 1255)
i:1255 use numpy:0.0140469074249
i:1255 use minpy:0.00331902503967
acceleration:4.23223906329
float64 : (1256, 1256) matmul (1256, 1256)
i:1256 use numpy:0.0175681114197
i:1256 use minpy:0.00815391540527
acceleration:2.15456140351
float64 : (1257, 1257) matmul (1257, 1257)
i:1257 use numpy:0.0176048278809
i:1257 use minpy:0.0047619342804
acceleration:3.69699093777
float64 : (1258, 1258) matmul (1258, 1258)
i:1258 use numpy:0.0143709182739
i:1258 use minpy:0.00332903862
acceleration:4.31683735587
float64 : (1259, 1259) matmul (1259, 1259)
i:1259 use numpy:0.0175819396973
i:1259 use minpy:0.00782108306885
acceleration:2.24801853433
float64 : (1260, 1260) matmul (1260, 1260)
i:1260 use numpy:0.0176901817322
i:1260 use minpy:0.00469303131104
acceleration:3.76945742735
float64 : (1261, 1261) matmul (1261, 1261)
i:1261 use numpy:0.0143899917603
i:1261 use minpy:0.00348401069641
acceleration:4.13029494286
float64 : (1262, 1262) matmul (1262, 1262)
i:1262 use numpy:0.0180139541626
i:1262 use minpy:0.00785493850708
acceleration:2.2933284769
float64 : (1263, 1263) matmul (1263, 1263)
i:1263 use numpy:0.0176391601562
i:1263 use minpy:0.00470113754272
acceleration:3.75210467593
float64 : (1264, 1264) matmul (1264, 1264)
i:1264 use numpy:0.0144958496094
i:1264 use minpy:0.00341296195984
acceleration:4.24729304925
float64 : (1265, 1265) matmul (1265, 1265)
i:1265 use numpy:0.0276818275452
i:1265 use minpy:0.00821089744568
acceleration:3.371352246
float64 : (1266, 1266) matmul (1266, 1266)
i:1266 use numpy:0.0176291465759
i:1266 use minpy:0.00471782684326
acceleration:3.73670911664
float64 : (1267, 1267) matmul (1267, 1267)
i:1267 use numpy:0.0151491165161
i:1267 use minpy:0.00337600708008
acceleration:4.48728813559
float64 : (1268, 1268) matmul (1268, 1268)
i:1268 use numpy:0.0179731845856
i:1268 use minpy:0.00792717933655
acceleration:2.26728623417
float64 : (1269, 1269) matmul (1269, 1269)
i:1269 use numpy:0.0181229114532
i:1269 use minpy:0.0047709941864
acceleration:3.79856079156
float64 : (1270, 1270) matmul (1270, 1270)
i:1270 use numpy:0.0147390365601
i:1270 use minpy:0.00340485572815
acceleration:4.32882851341
float64 : (1271, 1271) matmul (1271, 1271)
i:1271 use numpy:0.0182011127472
i:1271 use minpy:0.00799417495728
acceleration:2.2767968983
float64 : (1272, 1272) matmul (1272, 1272)
i:1272 use numpy:0.0179779529572
i:1272 use minpy:0.00475001335144
acceleration:3.78482156302
float64 : (1273, 1273) matmul (1273, 1273)
i:1273 use numpy:0.0478391647339
i:1273 use minpy:0.0187931060791
acceleration:2.54556987719
float64 : (1274, 1274) matmul (1274, 1274)
i:1274 use numpy:0.0182800292969
i:1274 use minpy:0.021185874939
acceleration:0.862840423138
float64 : (1275, 1275) matmul (1275, 1275)
i:1275 use numpy:0.0186660289764
i:1275 use minpy:0.02024102211
acceleration:0.922188063182
float64 : (1276, 1276) matmul (1276, 1276)
i:1276 use numpy:0.0185389518738
i:1276 use minpy:0.0206918716431
acceleration:0.895953357607
float64 : (1277, 1277) matmul (1277, 1277)
i:1277 use numpy:0.0185859203339
i:1277 use minpy:0.0195460319519
acceleration:0.950879461345
float64 : (1278, 1278) matmul (1278, 1278)
i:1278 use numpy:0.0154631137848
i:1278 use minpy:0.0190658569336
acceleration:0.811036914766
float64 : (1279, 1279) matmul (1279, 1279)
i:1279 use numpy:0.0186231136322
i:1279 use minpy:0.023824930191
acceleration:0.781664982137
float64 : (1280, 1280) matmul (1280, 1280)
i:1280 use numpy:0.0185542106628
i:1280 use minpy:0.00482797622681
acceleration:3.8430617284
float64 : (1281, 1281) matmul (1281, 1281)
i:1281 use numpy:0.0180060863495
i:1281 use minpy:0.00344395637512
acceleration:5.2283142956
float64 : (1282, 1282) matmul (1282, 1282)
i:1282 use numpy:0.0189518928528
i:1282 use minpy:0.00796294212341
acceleration:2.38001137759
float64 : (1283, 1283) matmul (1283, 1283)
i:1283 use numpy:0.0190072059631
i:1283 use minpy:0.00478601455688
acceleration:3.97140579855
float64 : (1284, 1284) matmul (1284, 1284)
i:1284 use numpy:0.0163359642029
i:1284 use minpy:0.0034339427948
acceleration:4.75720336041
float64 : (1285, 1285) matmul (1285, 1285)
i:1285 use numpy:0.0192289352417
i:1285 use minpy:0.00824880599976
acceleration:2.33111740563
float64 : (1286, 1286) matmul (1286, 1286)
i:1286 use numpy:0.0201709270477
i:1286 use minpy:0.00504612922668
acceleration:3.99730687456
float64 : (1287, 1287) matmul (1287, 1287)
i:1287 use numpy:0.0267188549042
i:1287 use minpy:0.0038800239563
acceleration:6.88626029249
float64 : (1288, 1288) matmul (1288, 1288)
i:1288 use numpy:0.0194039344788
i:1288 use minpy:0.00827097892761
acceleration:2.34602634689
float64 : (1289, 1289) matmul (1289, 1289)
i:1289 use numpy:0.0189950466156
i:1289 use minpy:0.00485396385193
acceleration:3.91330615453
float64 : (1290, 1290) matmul (1290, 1290)
i:1290 use numpy:0.0265350341797
i:1290 use minpy:0.0195319652557
acceleration:1.35854399863
float64 : (1291, 1291) matmul (1291, 1291)
i:1291 use numpy:0.0195119380951
i:1291 use minpy:0.00828099250793
acceleration:2.35623182564
float64 : (1292, 1292) matmul (1292, 1292)
i:1292 use numpy:0.0202629566193
i:1292 use minpy:0.00488090515137
acceleration:4.15147518562
float64 : (1293, 1293) matmul (1293, 1293)
i:1293 use numpy:0.0190529823303
i:1293 use minpy:0.00345015525818
acceleration:5.52235505494
float64 : (1294, 1294) matmul (1294, 1294)
i:1294 use numpy:0.0193359851837
i:1294 use minpy:0.00815200805664
acceleration:2.37192910622
float64 : (1295, 1295) matmul (1295, 1295)
i:1295 use numpy:0.0200388431549
i:1295 use minpy:0.0056939125061
acceleration:3.51934511347
float64 : (1296, 1296) matmul (1296, 1296)
i:1296 use numpy:0.0171239376068
i:1296 use minpy:0.00359392166138
acceleration:4.7646941754
float64 : (1297, 1297) matmul (1297, 1297)
i:1297 use numpy:0.0194549560547
i:1297 use minpy:0.00823402404785
acceleration:2.36275191105
float64 : (1298, 1298) matmul (1298, 1298)
i:1298 use numpy:0.0199151039124
i:1298 use minpy:0.00497698783875
acceleration:4.00143712575
float64 : (1299, 1299) matmul (1299, 1299)
i:1299 use numpy:0.0161681175232
i:1299 use minpy:0.00347805023193
acceleration:4.64861530025
float64 : (1300, 1300) matmul (1300, 1300)
i:1300 use numpy:0.0208961963654
i:1300 use minpy:0.00830411911011
acceleration:2.51636520241
float64 : (1301, 1301) matmul (1301, 1301)
i:1301 use numpy:0.0195739269257
i:1301 use minpy:0.00501799583435
acceleration:3.90074594954
float64 : (1302, 1302) matmul (1302, 1302)
i:1302 use numpy:0.0162320137024
i:1302 use minpy:0.00352597236633
acceleration:4.60355669755
float64 : (1303, 1303) matmul (1303, 1303)
i:1303 use numpy:0.0467939376831
i:1303 use minpy:0.0084171295166
acceleration:5.55937004305
float64 : (1304, 1304) matmul (1304, 1304)
i:1304 use numpy:0.0767109394073
i:1304 use minpy:0.00501203536987
acceleration:15.3053467796
float64 : (1305, 1305) matmul (1305, 1305)
i:1305 use numpy:0.0459017753601
i:1305 use minpy:0.00478506088257
acceleration:9.59272546089
float64 : (1306, 1306) matmul (1306, 1306)
i:1306 use numpy:0.0856168270111
i:1306 use minpy:0.027379989624
acceleration:3.12698537095
float64 : (1307, 1307) matmul (1307, 1307)
i:1307 use numpy:0.0197520256042
i:1307 use minpy:0.00498080253601
acceleration:3.96563113302
float64 : (1308, 1308) matmul (1308, 1308)
i:1308 use numpy:0.0167198181152
i:1308 use minpy:0.00352501869202
acceleration:4.74318566114
float64 : (1309, 1309) matmul (1309, 1309)
i:1309 use numpy:0.0502288341522
i:1309 use minpy:0.00851106643677
acceleration:5.90159112555
float64 : (1310, 1310) matmul (1310, 1310)
i:1310 use numpy:0.0218169689178
i:1310 use minpy:0.0490469932556
acceleration:0.444817663014
float64 : (1311, 1311) matmul (1311, 1311)
i:1311 use numpy:0.0167701244354
i:1311 use minpy:0.0479001998901
acceleration:0.350105520935
float64 : (1312, 1312) matmul (1312, 1312)
i:1312 use numpy:0.0213649272919
i:1312 use minpy:0.0244190692902
acceleration:0.874927993283
float64 : (1313, 1313) matmul (1313, 1313)
i:1313 use numpy:0.0246329307556
i:1313 use minpy:0.00577902793884
acceleration:4.26246957383
float64 : (1314, 1314) matmul (1314, 1314)
i:1314 use numpy:0.0183839797974
i:1314 use minpy:0.00368022918701
acceleration:4.99533557917
float64 : (1315, 1315) matmul (1315, 1315)
i:1315 use numpy:0.0208098888397
i:1315 use minpy:0.00852489471436
acceleration:2.44107282694
float64 : (1316, 1316) matmul (1316, 1316)
i:1316 use numpy:0.0218641757965
i:1316 use minpy:0.00510787963867
acceleration:4.2804798357
float64 : (1317, 1317) matmul (1317, 1317)
i:1317 use numpy:0.025869846344
i:1317 use minpy:0.0038058757782
acceleration:6.79734385767
float64 : (1318, 1318) matmul (1318, 1318)
i:1318 use numpy:0.0297679901123
i:1318 use minpy:0.00854086875916
acceleration:3.48535856852
float64 : (1319, 1319) matmul (1319, 1319)
i:1319 use numpy:0.0209939479828
i:1319 use minpy:0.00528693199158
acceleration:3.97091319053
float64 : (1320, 1320) matmul (1320, 1320)
i:1320 use numpy:0.0173380374908
i:1320 use minpy:0.00372195243835
acceleration:4.6583178528
float64 : (1321, 1321) matmul (1321, 1321)
i:1321 use numpy:0.0210309028625
i:1321 use minpy:0.00857496261597
acceleration:2.45259411667
float64 : (1322, 1322) matmul (1322, 1322)
i:1322 use numpy:0.02570104599
i:1322 use minpy:0.00508308410645
acceleration:5.05619136961
float64 : (1323, 1323) matmul (1323, 1323)
i:1323 use numpy:0.022027015686
i:1323 use minpy:0.00369596481323
acceleration:5.9597471294
float64 : (1324, 1324) matmul (1324, 1324)
i:1324 use numpy:0.0212559700012
i:1324 use minpy:0.0219850540161
acceleration:0.966837287989
float64 : (1325, 1325) matmul (1325, 1325)
i:1325 use numpy:0.021155834198
i:1325 use minpy:0.00520610809326
acceleration:4.06365634732
float64 : (1326, 1326) matmul (1326, 1326)
i:1326 use numpy:0.0177180767059
i:1326 use minpy:0.00378894805908
acceleration:4.67625220237
float64 : (1327, 1327) matmul (1327, 1327)
i:1327 use numpy:0.0214509963989
i:1327 use minpy:0.00868988037109
acceleration:2.46850307287
float64 : (1328, 1328) matmul (1328, 1328)
i:1328 use numpy:0.0211470127106
i:1328 use minpy:0.00519680976868
acceleration:4.06922971051
float64 : (1329, 1329) matmul (1329, 1329)
i:1329 use numpy:0.0180580615997
i:1329 use minpy:0.00373601913452
acceleration:4.83350350989
float64 : (1330, 1330) matmul (1330, 1330)
i:1330 use numpy:0.0264828205109
i:1330 use minpy:0.00867199897766
acceleration:3.05383113848
float64 : (1331, 1331) matmul (1331, 1331)
i:1331 use numpy:0.0209290981293
i:1331 use minpy:0.00526189804077
acceleration:3.97748074309
float64 : (1332, 1332) matmul (1332, 1332)
i:1332 use numpy:0.0173561573029
i:1332 use minpy:0.00372290611267
acceleration:4.66199167467
float64 : (1333, 1333) matmul (1333, 1333)
i:1333 use numpy:0.0210700035095
i:1333 use minpy:0.00878000259399
acceleration:2.39977190029
float64 : (1334, 1334) matmul (1334, 1334)
i:1334 use numpy:0.0207509994507
i:1334 use minpy:0.00518703460693
acceleration:4.00055157198
float64 : (1335, 1335) matmul (1335, 1335)
i:1335 use numpy:0.0174551010132
i:1335 use minpy:0.00373816490173
acceleration:4.66943044837
float64 : (1336, 1336) matmul (1336, 1336)
i:1336 use numpy:0.0214788913727
i:1336 use minpy:0.00883984565735
acceleration:2.42978126601
float64 : (1337, 1337) matmul (1337, 1337)
i:1337 use numpy:0.021192073822
i:1337 use minpy:0.00521183013916
acceleration:4.06614821592
float64 : (1338, 1338) matmul (1338, 1338)
i:1338 use numpy:0.0170700550079
i:1338 use minpy:0.00376605987549
acceleration:4.53260319068
float64 : (1339, 1339) matmul (1339, 1339)
i:1339 use numpy:0.0206568241119
i:1339 use minpy:0.00882005691528
acceleration:2.34202843704
float64 : (1340, 1340) matmul (1340, 1340)
i:1340 use numpy:0.0205709934235
i:1340 use minpy:0.00525498390198
acceleration:3.91456830452
float64 : (1341, 1341) matmul (1341, 1341)
i:1341 use numpy:0.0193409919739
i:1341 use minpy:0.00375914573669
acceleration:5.14504978753
float64 : (1342, 1342) matmul (1342, 1342)
i:1342 use numpy:0.0239250659943
i:1342 use minpy:0.00898098945618
acceleration:2.66396771881
float64 : (1343, 1343) matmul (1343, 1343)
i:1343 use numpy:0.0206751823425
i:1343 use minpy:0.00542521476746
acceleration:3.81094264997
float64 : (1344, 1344) matmul (1344, 1344)
i:1344 use numpy:0.0193121433258
i:1344 use minpy:0.00402021408081
acceleration:4.80375993358
float64 : (1345, 1345) matmul (1345, 1345)
i:1345 use numpy:0.0211589336395
i:1345 use minpy:0.00905609130859
acceleration:2.3364311289
float64 : (1346, 1346) matmul (1346, 1346)
i:1346 use numpy:0.0204629898071
i:1346 use minpy:0.00529193878174
acceleration:3.86682285096
float64 : (1347, 1347) matmul (1347, 1347)
i:1347 use numpy:0.0171298980713
i:1347 use minpy:0.00384092330933
acceleration:4.45983860956
float64 : (1348, 1348) matmul (1348, 1348)
i:1348 use numpy:0.0210468769073
i:1348 use minpy:0.00892090797424
acceleration:2.35927519577
float64 : (1349, 1349) matmul (1349, 1349)
i:1349 use numpy:0.0216979980469
i:1349 use minpy:0.00542616844177
acceleration:3.99876971747
float64 : (1350, 1350) matmul (1350, 1350)
i:1350 use numpy:0.017657995224
i:1350 use minpy:0.00386714935303
acceleration:4.56615289766
float64 : (1351, 1351) matmul (1351, 1351)
i:1351 use numpy:0.024206161499
i:1351 use minpy:0.00914597511292
acceleration:2.64664633352
float64 : (1352, 1352) matmul (1352, 1352)
i:1352 use numpy:0.0220251083374
i:1352 use minpy:0.0053379535675
acceleration:4.12613336907
float64 : (1353, 1353) matmul (1353, 1353)
i:1353 use numpy:0.0174570083618
i:1353 use minpy:0.00386691093445
acceleration:4.51445835132
float64 : (1354, 1354) matmul (1354, 1354)
i:1354 use numpy:0.0215389728546
i:1354 use minpy:0.00893998146057
acceleration:2.40928607622
float64 : (1355, 1355) matmul (1355, 1355)
i:1355 use numpy:0.0210740566254
i:1355 use minpy:0.00540804862976
acceleration:3.89679495658
float64 : (1356, 1356) matmul (1356, 1356)
i:1356 use numpy:0.0186200141907
i:1356 use minpy:0.00390100479126
acceleration:4.77313286884
float64 : (1357, 1357) matmul (1357, 1357)
i:1357 use numpy:0.0217649936676
i:1357 use minpy:0.00914788246155
acceleration:2.37923844771
float64 : (1358, 1358) matmul (1358, 1358)
i:1358 use numpy:0.0288510322571
i:1358 use minpy:0.00585293769836
acceleration:4.92932502342
float64 : (1359, 1359) matmul (1359, 1359)
i:1359 use numpy:0.0207550525665
i:1359 use minpy:0.00390195846558
acceleration:5.31913723573
float64 : (1360, 1360) matmul (1360, 1360)
i:1360 use numpy:0.0220940113068
i:1360 use minpy:0.00915312767029
acceleration:2.41382094762
float64 : (1361, 1361) matmul (1361, 1361)
i:1361 use numpy:0.031986951828
i:1361 use minpy:0.00600290298462
acceleration:5.32858050679
float64 : (1362, 1362) matmul (1362, 1362)
i:1362 use numpy:0.021388053894
i:1362 use minpy:0.00394701957703
acceleration:5.4187858653
float64 : (1363, 1363) matmul (1363, 1363)
i:1363 use numpy:0.0225911140442
i:1363 use minpy:0.00924777984619
acceleration:2.44286892853
float64 : (1364, 1364) matmul (1364, 1364)
i:1364 use numpy:0.0350630283356
i:1364 use minpy:0.00605607032776
acceleration:5.78973268769
float64 : (1365, 1365) matmul (1365, 1365)
i:1365 use numpy:0.0192089080811
i:1365 use minpy:0.00395202636719
acceleration:4.86052123552
float64 : (1366, 1366) matmul (1366, 1366)
i:1366 use numpy:0.0402309894562
i:1366 use minpy:0.00921106338501
acceleration:4.36768131697
float64 : (1367, 1367) matmul (1367, 1367)
i:1367 use numpy:0.0269770622253
i:1367 use minpy:0.0066192150116
acceleration:4.07556820228
float64 : (1368, 1368) matmul (1368, 1368)
i:1368 use numpy:0.0219910144806
i:1368 use minpy:0.00403785705566
acceleration:5.44620925838
float64 : (1369, 1369) matmul (1369, 1369)
i:1369 use numpy:0.0230150222778
i:1369 use minpy:0.00930094718933
acceleration:2.47448155648
float64 : (1370, 1370) matmul (1370, 1370)
i:1370 use numpy:0.0223388671875
i:1370 use minpy:0.00555992126465
acceleration:4.01783876501
float64 : (1371, 1371) matmul (1371, 1371)
i:1371 use numpy:0.0193529129028
i:1371 use minpy:0.00426983833313
acceleration:4.53246970797
float64 : (1372, 1372) matmul (1372, 1372)
i:1372 use numpy:0.0235619544983
i:1372 use minpy:0.00934600830078
acceleration:2.52107142857
float64 : (1373, 1373) matmul (1373, 1373)
i:1373 use numpy:0.0226221084595
i:1373 use minpy:0.00554704666138
acceleration:4.07822573713
float64 : (1374, 1374) matmul (1374, 1374)
i:1374 use numpy:0.0194489955902
i:1374 use minpy:0.00408291816711
acceleration:4.76350364964
float64 : (1375, 1375) matmul (1375, 1375)
i:1375 use numpy:0.0227711200714
i:1375 use minpy:0.00926303863525
acceleration:2.45827756615
float64 : (1376, 1376) matmul (1376, 1376)
i:1376 use numpy:0.0248589515686
i:1376 use minpy:0.00559496879578
acceleration:4.44309029701
float64 : (1377, 1377) matmul (1377, 1377)
i:1377 use numpy:0.0189411640167
i:1377 use minpy:0.00397896766663
acceleration:4.76032116963
float64 : (1378, 1378) matmul (1378, 1378)
i:1378 use numpy:0.0283100605011
i:1378 use minpy:0.00955820083618
acceleration:2.96186081317
float64 : (1379, 1379) matmul (1379, 1379)
i:1379 use numpy:0.0261750221252
i:1379 use minpy:0.00662899017334
acceleration:3.94856855129
float64 : (1380, 1380) matmul (1380, 1380)
i:1380 use numpy:0.0193610191345
i:1380 use minpy:0.00400900840759
acceleration:4.82937853107
float64 : (1381, 1381) matmul (1381, 1381)
i:1381 use numpy:0.0239520072937
i:1381 use minpy:0.00937795639038
acceleration:2.55407535466
float64 : (1382, 1382) matmul (1382, 1382)
i:1382 use numpy:0.0305960178375
i:1382 use minpy:0.00593709945679
acceleration:5.15336117581
float64 : (1383, 1383) matmul (1383, 1383)
i:1383 use numpy:0.0194971561432
i:1383 use minpy:0.00406098365784
acceleration:4.80109199789
float64 : (1384, 1384) matmul (1384, 1384)
i:1384 use numpy:0.0229170322418
i:1384 use minpy:0.00940799713135
acceleration:2.43590978206
float64 : (1385, 1385) matmul (1385, 1385)
i:1385 use numpy:0.0260121822357
i:1385 use minpy:0.00555014610291
acceleration:4.68675630396
float64 : (1386, 1386) matmul (1386, 1386)
i:1386 use numpy:0.0197491645813
i:1386 use minpy:0.00404000282288
acceleration:4.8884036589
float64 : (1387, 1387) matmul (1387, 1387)
i:1387 use numpy:0.0232698917389
i:1387 use minpy:0.00940704345703
acceleration:2.47366686942
float64 : (1388, 1388) matmul (1388, 1388)
i:1388 use numpy:0.0273280143738
i:1388 use minpy:0.00573778152466
acceleration:4.76281891465
float64 : (1389, 1389) matmul (1389, 1389)
i:1389 use numpy:0.0194659233093
i:1389 use minpy:0.00401091575623
acceleration:4.85323664031
float64 : (1390, 1390) matmul (1390, 1390)
i:1390 use numpy:0.0235471725464
i:1390 use minpy:0.00944018363953
acceleration:2.4943553479
float64 : (1391, 1391) matmul (1391, 1391)
i:1391 use numpy:0.0227589607239
i:1391 use minpy:0.00618600845337
acceleration:3.67910275187
float64 : (1392, 1392) matmul (1392, 1392)
i:1392 use numpy:0.0189809799194
i:1392 use minpy:0.00407099723816
acceleration:4.66248901903
float64 : (1393, 1393) matmul (1393, 1393)
i:1393 use numpy:0.0255138874054
i:1393 use minpy:0.00948786735535
acceleration:2.68910667169
float64 : (1394, 1394) matmul (1394, 1394)
i:1394 use numpy:0.0246520042419
i:1394 use minpy:0.00569796562195
acceleration:4.32645717394
float64 : (1395, 1395) matmul (1395, 1395)
i:1395 use numpy:0.0193200111389
i:1395 use minpy:0.00407099723816
acceleration:4.74576866764
float64 : (1396, 1396) matmul (1396, 1396)
i:1396 use numpy:0.0304520130157
i:1396 use minpy:0.00950503349304
acceleration:3.20377756039
float64 : (1397, 1397) matmul (1397, 1397)
i:1397 use numpy:0.0233590602875
i:1397 use minpy:0.00572609901428
acceleration:4.07940209019
float64 : (1398, 1398) matmul (1398, 1398)
i:1398 use numpy:0.0233149528503
i:1398 use minpy:0.00415301322937
acceleration:5.61398472932
float64 : (1399, 1399) matmul (1399, 1399)
i:1399 use numpy:0.0237560272217
i:1399 use minpy:0.00958299636841
acceleration:2.47897696174
float64 : (1400, 1400) matmul (1400, 1400)
i:1400 use numpy:0.0235919952393
i:1400 use minpy:0.00568604469299
acceleration:4.14910478427
float64 : (1401, 1401) matmul (1401, 1401)
i:1401 use numpy:0.0206680297852
i:1401 use minpy:0.00414800643921
acceleration:4.98264168295
float64 : (1402, 1402) matmul (1402, 1402)
i:1402 use numpy:0.0239360332489
i:1402 use minpy:0.00970697402954
acceleration:2.46585940954
float64 : (1403, 1403) matmul (1403, 1403)
i:1403 use numpy:0.0235788822174
i:1403 use minpy:0.00571489334106
acceleration:4.12586566542
float64 : (1404, 1404) matmul (1404, 1404)
i:1404 use numpy:0.020229101181
i:1404 use minpy:0.00411105155945
acceleration:4.92066345763
float64 : (1405, 1405) matmul (1405, 1405)
i:1405 use numpy:0.0245621204376
i:1405 use minpy:0.0096640586853
acceleration:2.54159471061
float64 : (1406, 1406) matmul (1406, 1406)
i:1406 use numpy:0.0244719982147
i:1406 use minpy:0.00644588470459
acceleration:3.79653055186
float64 : (1407, 1407) matmul (1407, 1407)
i:1407 use numpy:0.0207278728485
i:1407 use minpy:0.00416207313538
acceleration:4.98017987054
float64 : (1408, 1408) matmul (1408, 1408)
i:1408 use numpy:0.0248429775238
i:1408 use minpy:0.0097770690918
acceleration:2.54094323059
float64 : (1409, 1409) matmul (1409, 1409)
i:1409 use numpy:0.0241701602936
i:1409 use minpy:0.00579309463501
acceleration:4.17223639806
float64 : (1410, 1410) matmul (1410, 1410)
i:1410 use numpy:0.0204610824585
i:1410 use minpy:0.00418591499329
acceleration:4.88807882896
float64 : (1411, 1411) matmul (1411, 1411)
i:1411 use numpy:0.0246441364288
i:1411 use minpy:0.00970005989075
acceleration:2.54061693499
float64 : (1412, 1412) matmul (1412, 1412)
i:1412 use numpy:0.0246388912201
i:1412 use minpy:0.00577306747437
acceleration:4.26790286611
float64 : (1413, 1413) matmul (1413, 1413)
i:1413 use numpy:0.0207221508026
i:1413 use minpy:0.00420904159546
acceleration:4.92324685624
float64 : (1414, 1414) matmul (1414, 1414)
i:1414 use numpy:0.02614402771
i:1414 use minpy:0.00971293449402
acceleration:2.69167137141
float64 : (1415, 1415) matmul (1415, 1415)
i:1415 use numpy:0.0525197982788
i:1415 use minpy:0.00653600692749
acceleration:8.03545633618
float64 : (1416, 1416) matmul (1416, 1416)
i:1416 use numpy:0.0282897949219
i:1416 use minpy:0.00499296188354
acceleration:5.66593448572
float64 : (1417, 1417) matmul (1417, 1417)
i:1417 use numpy:0.0249948501587
i:1417 use minpy:0.00985598564148
acceleration:2.53600716031
float64 : (1418, 1418) matmul (1418, 1418)
i:1418 use numpy:0.0253639221191
i:1418 use minpy:0.00610780715942
acceleration:4.15270512921
float64 : (1419, 1419) matmul (1419, 1419)
i:1419 use numpy:0.0212411880493
i:1419 use minpy:0.00419592857361
acceleration:5.06233308711
float64 : (1420, 1420) matmul (1420, 1420)
i:1420 use numpy:0.0250008106232
i:1420 use minpy:0.00985407829285
acceleration:2.53710290097
float64 : (1421, 1421) matmul (1421, 1421)
i:1421 use numpy:0.0247189998627
i:1421 use minpy:0.00584506988525
acceleration:4.22903410018
float64 : (1422, 1422) matmul (1422, 1422)
i:1422 use numpy:0.0208740234375
i:1422 use minpy:0.00420689582825
acceleration:4.96185888354
float64 : (1423, 1423) matmul (1423, 1423)
i:1423 use numpy:0.0251069068909
i:1423 use minpy:0.0098729133606
acceleration:2.54300893504
float64 : (1424, 1424) matmul (1424, 1424)
i:1424 use numpy:0.07648396492
i:1424 use minpy:0.00622606277466
acceleration:12.2844834189
float64 : (1425, 1425) matmul (1425, 1425)
i:1425 use numpy:0.03329205513
i:1425 use minpy:0.00460386276245
acceleration:7.23133091662
float64 : (1426, 1426) matmul (1426, 1426)
i:1426 use numpy:0.0257179737091
i:1426 use minpy:0.00988698005676
acceleration:2.60119607418
float64 : (1427, 1427) matmul (1427, 1427)
i:1427 use numpy:0.0253570079803
i:1427 use minpy:0.00600504875183
acceleration:4.22261484099
float64 : (1428, 1428) matmul (1428, 1428)
i:1428 use numpy:0.0215768814087
i:1428 use minpy:0.00429201126099
acceleration:5.02721919787
float64 : (1429, 1429) matmul (1429, 1429)
i:1429 use numpy:0.0260961055756
i:1429 use minpy:0.00999712944031
acceleration:2.61035987694
float64 : (1430, 1430) matmul (1430, 1430)
i:1430 use numpy:0.0253028869629
i:1430 use minpy:0.00592613220215
acceleration:4.26971355005
float64 : (1431, 1431) matmul (1431, 1431)
i:1431 use numpy:0.0211658477783
i:1431 use minpy:0.00432109832764
acceleration:4.89825645553
float64 : (1432, 1432) matmul (1432, 1432)
i:1432 use numpy:0.0260410308838
i:1432 use minpy:0.0103850364685
acceleration:2.50755314753
float64 : (1433, 1433) matmul (1433, 1433)
i:1433 use numpy:0.0262792110443
i:1433 use minpy:0.00607705116272
acceleration:4.32433598807
float64 : (1434, 1434) matmul (1434, 1434)
i:1434 use numpy:0.0210781097412
i:1434 use minpy:0.00429081916809
acceleration:4.9123742846
float64 : (1435, 1435) matmul (1435, 1435)
i:1435 use numpy:0.0295670032501
i:1435 use minpy:0.0102319717407
acceleration:2.88966818902
float64 : (1436, 1436) matmul (1436, 1436)
i:1436 use numpy:0.0271339416504
i:1436 use minpy:0.00630903244019
acceleration:4.30080870682
float64 : (1437, 1437) matmul (1437, 1437)
i:1437 use numpy:0.0213708877563
i:1437 use minpy:0.00441694259644
acceleration:4.83838929073
float64 : (1438, 1438) matmul (1438, 1438)
i:1438 use numpy:0.025515794754
i:1438 use minpy:0.0102910995483
acceleration:2.47940413307
float64 : (1439, 1439) matmul (1439, 1439)
i:1439 use numpy:0.0253999233246
i:1439 use minpy:0.00601005554199
acceleration:4.22623770232
float64 : (1440, 1440) matmul (1440, 1440)
i:1440 use numpy:0.0206599235535
i:1440 use minpy:0.00433707237244
acceleration:4.76356439998
float64 : (1441, 1441) matmul (1441, 1441)
i:1441 use numpy:0.102683067322
i:1441 use minpy:0.0101661682129
acceleration:10.1004690432
float64 : (1442, 1442) matmul (1442, 1442)
i:1442 use numpy:0.0249869823456
i:1442 use minpy:0.00605583190918
acceleration:4.1261023622
float64 : (1443, 1443) matmul (1443, 1443)
i:1443 use numpy:0.0212848186493
i:1443 use minpy:0.0043580532074
acceleration:4.88401991356
float64 : (1444, 1444) matmul (1444, 1444)
i:1444 use numpy:0.0301349163055
i:1444 use minpy:0.0106298923492
acceleration:2.83492205899
float64 : (1445, 1445) matmul (1445, 1445)
i:1445 use numpy:0.0255980491638
i:1445 use minpy:0.00604104995728
acceleration:4.23735101429
float64 : (1446, 1446) matmul (1446, 1446)
i:1446 use numpy:0.0242700576782
i:1446 use minpy:0.00434803962708
acceleration:5.58183911828
float64 : (1447, 1447) matmul (1447, 1447)
i:1447 use numpy:0.0260579586029
i:1447 use minpy:0.0102560520172
acceleration:2.54073970756
float64 : (1448, 1448) matmul (1448, 1448)
i:1448 use numpy:0.0252728462219
i:1448 use minpy:0.00609302520752
acceleration:4.14783221161
float64 : (1449, 1449) matmul (1449, 1449)
i:1449 use numpy:0.021665096283
i:1449 use minpy:0.0044059753418
acceleration:4.91720779221
float64 : (1450, 1450) matmul (1450, 1450)
i:1450 use numpy:0.026181936264
i:1450 use minpy:0.010330915451
acceleration:2.53432877155
float64 : (1451, 1451) matmul (1451, 1451)
i:1451 use numpy:0.0259001255035
i:1451 use minpy:0.00619101524353
acceleration:4.18350213733
float64 : (1452, 1452) matmul (1452, 1452)
i:1452 use numpy:0.0331449508667
i:1452 use minpy:0.00439882278442
acceleration:7.53495934959
float64 : (1453, 1453) matmul (1453, 1453)
i:1453 use numpy:0.0319268703461
i:1453 use minpy:0.0104460716248
acceleration:3.05635185101
float64 : (1454, 1454) matmul (1454, 1454)
i:1454 use numpy:0.02610206604
i:1454 use minpy:0.00610399246216
acceleration:4.27622841965
float64 : (1455, 1455) matmul (1455, 1455)
i:1455 use numpy:0.0218420028687
i:1455 use minpy:0.00437712669373
acceleration:4.99003213683
float64 : (1456, 1456) matmul (1456, 1456)
i:1456 use numpy:0.026223897934
i:1456 use minpy:0.0103960037231
acceleration:2.52249793597
float64 : (1457, 1457) matmul (1457, 1457)
i:1457 use numpy:0.0421130657196
i:1457 use minpy:0.0188610553741
acceleration:2.23280536981
float64 : (1458, 1458) matmul (1458, 1458)
i:1458 use numpy:0.0217671394348
i:1458 use minpy:0.00438499450684
acceleration:4.9640060896
float64 : (1459, 1459) matmul (1459, 1459)
i:1459 use numpy:0.0306448936462
i:1459 use minpy:0.0105459690094
acceleration:2.90583953157
float64 : (1460, 1460) matmul (1460, 1460)
i:1460 use numpy:0.0261719226837
i:1460 use minpy:0.00629496574402
acceleration:4.15759572776
float64 : (1461, 1461) matmul (1461, 1461)
i:1461 use numpy:0.0311689376831
i:1461 use minpy:0.0047299861908
acceleration:6.58964665558
float64 : (1462, 1462) matmul (1462, 1462)
i:1462 use numpy:0.0329720973969
i:1462 use minpy:0.010792016983
acceleration:3.05523031039
float64 : (1463, 1463) matmul (1463, 1463)
i:1463 use numpy:0.0269091129303
i:1463 use minpy:0.00622892379761
acceleration:4.32002602771
float64 : (1464, 1464) matmul (1464, 1464)
i:1464 use numpy:0.0224649906158
i:1464 use minpy:0.00447916984558
acceleration:5.01543620589
float64 : (1465, 1465) matmul (1465, 1465)
i:1465 use numpy:0.0270478725433
i:1465 use minpy:0.0105540752411
acceleration:2.56278943683
float64 : (1466, 1466) matmul (1466, 1466)
i:1466 use numpy:0.0371730327606
i:1466 use minpy:0.00635695457458
acceleration:5.84761654728
float64 : (1467, 1467) matmul (1467, 1467)
i:1467 use numpy:0.0233352184296
i:1467 use minpy:0.00439620018005
acceleration:5.30804273551
float64 : (1468, 1468) matmul (1468, 1468)
i:1468 use numpy:0.0317997932434
i:1468 use minpy:0.0112590789795
acceleration:2.82436896493
float64 : (1469, 1469) matmul (1469, 1469)
i:1469 use numpy:0.0269639492035
i:1469 use minpy:0.00624418258667
acceleration:4.31825124093
float64 : (1470, 1470) matmul (1470, 1470)
i:1470 use numpy:0.0232400894165
i:1470 use minpy:0.00455808639526
acceleration:5.09865048645
float64 : (1471, 1471) matmul (1471, 1471)
i:1471 use numpy:0.0283081531525
i:1471 use minpy:0.0106868743896
acceleration:2.64887114046
float64 : (1472, 1472) matmul (1472, 1472)
i:1472 use numpy:0.0310368537903
i:1472 use minpy:0.00642704963684
acceleration:4.82909819342
float64 : (1473, 1473) matmul (1473, 1473)
i:1473 use numpy:0.0245950222015
i:1473 use minpy:0.00452303886414
acceleration:5.43772073164
float64 : (1474, 1474) matmul (1474, 1474)
i:1474 use numpy:0.0292510986328
i:1474 use minpy:0.0107598304749
acceleration:2.71854642145
float64 : (1475, 1475) matmul (1475, 1475)
i:1475 use numpy:0.0275678634644
i:1475 use minpy:0.00628590583801
acceleration:4.38566281054
float64 : (1476, 1476) matmul (1476, 1476)
i:1476 use numpy:0.0274031162262
i:1476 use minpy:0.00508594512939
acceleration:5.38800862554
float64 : (1477, 1477) matmul (1477, 1477)
i:1477 use numpy:0.0315299034119
i:1477 use minpy:0.0107860565186
acceleration:2.92320954907
float64 : (1478, 1478) matmul (1478, 1478)
i:1478 use numpy:0.0275750160217
i:1478 use minpy:0.00630593299866
acceleration:4.37286853945
float64 : (1479, 1479) matmul (1479, 1479)
i:1479 use numpy:0.0396671295166
i:1479 use minpy:0.00581502914429
acceleration:6.82148421484
float64 : (1480, 1480) matmul (1480, 1480)
i:1480 use numpy:0.0350689888
i:1480 use minpy:0.0109550952911
acceleration:3.20115780539
float64 : (1481, 1481) matmul (1481, 1481)
i:1481 use numpy:0.0289671421051
i:1481 use minpy:0.00641489028931
acceleration:4.51560990114
float64 : (1482, 1482) matmul (1482, 1482)
i:1482 use numpy:0.0407731533051
i:1482 use minpy:0.00468897819519
acceleration:8.69553058423
float64 : (1483, 1483) matmul (1483, 1483)
i:1483 use numpy:0.0296759605408
i:1483 use minpy:0.0108880996704
acceleration:2.72554086012
float64 : (1484, 1484) matmul (1484, 1484)
i:1484 use numpy:0.0376288890839
i:1484 use minpy:0.00659894943237
acceleration:5.70225449816
float64 : (1485, 1485) matmul (1485, 1485)
i:1485 use numpy:0.0281820297241
i:1485 use minpy:0.00463008880615
acceleration:6.08671472709
float64 : (1486, 1486) matmul (1486, 1486)
i:1486 use numpy:0.028685092926
i:1486 use minpy:0.0108728408813
acceleration:2.63823348829
float64 : (1487, 1487) matmul (1487, 1487)
i:1487 use numpy:0.0282211303711
i:1487 use minpy:0.00661182403564
acceleration:4.26828212895
float64 : (1488, 1488) matmul (1488, 1488)
i:1488 use numpy:0.0239899158478
i:1488 use minpy:0.00454807281494
acceleration:5.27474313273
float64 : (1489, 1489) matmul (1489, 1489)
i:1489 use numpy:0.0344760417938
i:1489 use minpy:0.0116059780121
acceleration:2.97054171203
float64 : (1490, 1490) matmul (1490, 1490)
i:1490 use numpy:0.0282111167908
i:1490 use minpy:0.00648093223572
acceleration:4.35294117647
float64 : (1491, 1491) matmul (1491, 1491)
i:1491 use numpy:0.0260369777679
i:1491 use minpy:0.0170321464539
acceleration:1.5286962121
float64 : (1492, 1492) matmul (1492, 1492)
i:1492 use numpy:0.0284321308136
i:1492 use minpy:0.0109148025513
acceleration:2.60491480996
float64 : (1493, 1493) matmul (1493, 1493)
i:1493 use numpy:0.0289371013641
i:1493 use minpy:0.00651621818542
acceleration:4.44078153013
float64 : (1494, 1494) matmul (1494, 1494)
i:1494 use numpy:0.0243549346924
i:1494 use minpy:0.00463199615479
acceleration:5.25797817583
float64 : (1495, 1495) matmul (1495, 1495)
i:1495 use numpy:0.0293049812317
i:1495 use minpy:0.0110759735107
acceleration:2.64581539521
float64 : (1496, 1496) matmul (1496, 1496)
i:1496 use numpy:0.0294661521912
i:1496 use minpy:0.00645995140076
acceleration:4.56135818417
float64 : (1497, 1497) matmul (1497, 1497)
i:1497 use numpy:0.0266029834747
i:1497 use minpy:0.00467586517334
acceleration:5.68942484193
float64 : (1498, 1498) matmul (1498, 1498)
i:1498 use numpy:0.0352799892426
i:1498 use minpy:0.0115900039673
acceleration:3.04400148112
float64 : (1499, 1499) matmul (1499, 1499)
i:1499 use numpy:0.0397918224335
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acceleration:5.95068991336
float64 : (1500, 1500) matmul (1500, 1500)
i:1500 use numpy:0.0268189907074
i:1500 use minpy:0.0057270526886
acceleration:4.68286083011
float64 : (1501, 1501) matmul (1501, 1501)
i:1501 use numpy:0.0457479953766
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acceleration:4.01298755621
float64 : (1502, 1502) matmul (1502, 1502)
i:1502 use numpy:0.038948059082
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acceleration:5.22584772873
float64 : (1503, 1503) matmul (1503, 1503)
i:1503 use numpy:0.0502779483795
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acceleration:9.06235496347
float64 : (1504, 1504) matmul (1504, 1504)
i:1504 use numpy:0.0671811103821
i:1504 use minpy:0.0259039402008
acceleration:2.59347071763
float64 : (1505, 1505) matmul (1505, 1505)
i:1505 use numpy:0.0312969684601
i:1505 use minpy:0.00653886795044
acceleration:4.78629767374
float64 : (1506, 1506) matmul (1506, 1506)
i:1506 use numpy:0.0267159938812
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acceleration:5.49477761977
float64 : (1507, 1507) matmul (1507, 1507)
i:1507 use numpy:0.0305700302124
i:1507 use minpy:0.0110459327698
acceleration:2.76753723289
float64 : (1508, 1508) matmul (1508, 1508)
i:1508 use numpy:0.0305659770966
i:1508 use minpy:0.00653505325317
acceleration:4.67723458592
float64 : (1509, 1509) matmul (1509, 1509)
i:1509 use numpy:0.0580840110779
i:1509 use minpy:0.00483584403992
acceleration:12.0111423359
float64 : (1510, 1510) matmul (1510, 1510)
i:1510 use numpy:0.0343499183655
i:1510 use minpy:0.0111870765686
acceleration:3.07049997869
float64 : (1511, 1511) matmul (1511, 1511)
i:1511 use numpy:0.037132024765
i:1511 use minpy:0.00706076622009
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float64 : (1512, 1512) matmul (1512, 1512)
i:1512 use numpy:0.0307531356812
i:1512 use minpy:0.00488710403442
acceleration:6.29271148405
float64 : (1513, 1513) matmul (1513, 1513)
i:1513 use numpy:0.0340819358826
i:1513 use minpy:0.0113790035248
acceleration:2.99515997234
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i:1514 use numpy:0.029669046402
i:1514 use minpy:0.00657510757446
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float64 : (1515, 1515) matmul (1515, 1515)
i:1515 use numpy:0.0294210910797
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acceleration:5.89138737706
float64 : (1516, 1516) matmul (1516, 1516)
i:1516 use numpy:0.0411159992218
i:1516 use minpy:0.0114469528198
acceleration:3.59187286512
float64 : (1517, 1517) matmul (1517, 1517)
i:1517 use numpy:0.0316870212555
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acceleration:4.42058872443
float64 : (1518, 1518) matmul (1518, 1518)
i:1518 use numpy:0.0473420619965
i:1518 use minpy:0.00533699989319
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float64 : (1519, 1519) matmul (1519, 1519)
i:1519 use numpy:0.0384058952332
i:1519 use minpy:0.0112149715424
acceleration:3.42452007908
float64 : (1520, 1520) matmul (1520, 1520)
i:1520 use numpy:0.0301568508148
i:1520 use minpy:0.00656294822693
acceleration:4.59501580267
float64 : (1521, 1521) matmul (1521, 1521)
i:1521 use numpy:0.0282070636749
i:1521 use minpy:0.0057098865509
acceleration:4.94003925007
float64 : (1522, 1522) matmul (1522, 1522)
i:1522 use numpy:0.0306489467621
i:1522 use minpy:0.0112040042877
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float64 : (1523, 1523) matmul (1523, 1523)
i:1523 use numpy:0.0358428955078
i:1523 use minpy:0.00672101974487
acceleration:5.33295494856
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i:1524 use numpy:0.026741027832
i:1524 use minpy:0.00489497184753
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float64 : (1525, 1525) matmul (1525, 1525)
i:1525 use numpy:0.033704996109
i:1525 use minpy:0.0114841461182
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i:1526 use numpy:0.0388970375061
i:1526 use minpy:0.00670909881592
acceleration:5.79765458422
float64 : (1527, 1527) matmul (1527, 1527)
i:1527 use numpy:0.0330250263214
i:1527 use minpy:0.00531005859375
acceleration:6.21933369253
float64 : (1528, 1528) matmul (1528, 1528)
i:1528 use numpy:0.0352218151093
i:1528 use minpy:0.0114510059357
acceleration:3.07587082804
float64 : (1529, 1529) matmul (1529, 1529)
i:1529 use numpy:0.029923915863
i:1529 use minpy:0.00673699378967
acceleration:4.44173125243
float64 : (1530, 1530) matmul (1530, 1530)
i:1530 use numpy:0.0281209945679
i:1530 use minpy:0.00563097000122
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float64 : (1531, 1531) matmul (1531, 1531)
i:1531 use numpy:0.033910036087
i:1531 use minpy:0.0113139152527
acceleration:2.99719728579
float64 : (1532, 1532) matmul (1532, 1532)
i:1532 use numpy:0.029000043869
i:1532 use minpy:0.00671219825745
acceleration:4.32049870351
float64 : (1533, 1533) matmul (1533, 1533)
i:1533 use numpy:0.0249381065369
i:1533 use minpy:0.00494313240051
acceleration:5.04500072348
float64 : (1534, 1534) matmul (1534, 1534)
i:1534 use numpy:0.0299761295319
i:1534 use minpy:0.0112209320068
acceleration:2.67144739079
float64 : (1535, 1535) matmul (1535, 1535)
i:1535 use numpy:0.0290780067444
i:1535 use minpy:0.00683283805847
acceleration:4.25562650476
float64 : (1536, 1536) matmul (1536, 1536)
i:1536 use numpy:0.0253849029541
i:1536 use minpy:0.00496506690979
acceleration:5.11270108043
float64 : (1537, 1537) matmul (1537, 1537)
i:1537 use numpy:0.0304119586945
i:1537 use minpy:0.0115230083466
acceleration:2.63923775631
float64 : (1538, 1538) matmul (1538, 1538)
i:1538 use numpy:0.0292921066284
i:1538 use minpy:0.00671410560608
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float64 : (1539, 1539) matmul (1539, 1539)
i:1539 use numpy:0.0251271724701
i:1539 use minpy:0.00484895706177
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float64 : (1540, 1540) matmul (1540, 1540)
i:1540 use numpy:0.0344958305359
i:1540 use minpy:0.0113301277161
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float64 : (1541, 1541) matmul (1541, 1541)
i:1541 use numpy:0.0300929546356
i:1541 use minpy:0.00687384605408
acceleration:4.37789185252
float64 : (1542, 1542) matmul (1542, 1542)
i:1542 use numpy:0.0312230587006
i:1542 use minpy:0.00501489639282
acceleration:6.22606256537
float64 : (1543, 1543) matmul (1543, 1543)
i:1543 use numpy:0.0335619449615
i:1543 use minpy:0.013011932373
acceleration:2.57932058047
float64 : (1544, 1544) matmul (1544, 1544)
i:1544 use numpy:0.0467147827148
i:1544 use minpy:0.00698208808899
acceleration:6.69066074782
float64 : (1545, 1545) matmul (1545, 1545)
i:1545 use numpy:0.0259020328522
i:1545 use minpy:0.00485301017761
acceleration:5.33731269958
float64 : (1546, 1546) matmul (1546, 1546)
i:1546 use numpy:0.0495319366455
i:1546 use minpy:0.0115311145782
acceleration:4.29550294635
float64 : (1547, 1547) matmul (1547, 1547)
i:1547 use numpy:0.0338308811188
i:1547 use minpy:0.00708603858948
acceleration:4.77430099929
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i:1548 use numpy:0.029825925827
i:1548 use minpy:0.00487685203552
acceleration:6.11581520411
float64 : (1549, 1549) matmul (1549, 1549)
i:1549 use numpy:0.0527160167694
i:1549 use minpy:0.0117959976196
acceleration:4.4689748565
float64 : (1550, 1550) matmul (1550, 1550)
i:1550 use numpy:0.0337800979614
i:1550 use minpy:0.00680088996887
acceleration:4.96701139351
float64 : (1551, 1551) matmul (1551, 1551)
i:1551 use numpy:0.026004076004
i:1551 use minpy:0.00490522384644
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float64 : (1552, 1552) matmul (1552, 1552)
i:1552 use numpy:0.0307621955872
i:1552 use minpy:0.0119390487671
acceleration:2.57660356258
float64 : (1553, 1553) matmul (1553, 1553)
i:1553 use numpy:0.0493478775024
i:1553 use minpy:0.0232079029083
acceleration:2.12633936368
float64 : (1554, 1554) matmul (1554, 1554)
i:1554 use numpy:0.0294809341431
i:1554 use minpy:0.0209038257599
acceleration:1.41031285286
float64 : (1555, 1555) matmul (1555, 1555)
i:1555 use numpy:0.0368051528931
i:1555 use minpy:0.0250730514526
acceleration:1.4679167776
float64 : (1556, 1556) matmul (1556, 1556)
i:1556 use numpy:0.0329191684723
i:1556 use minpy:0.00732707977295
acceleration:4.49280879865
float64 : (1557, 1557) matmul (1557, 1557)
i:1557 use numpy:0.0341949462891
i:1557 use minpy:0.00495910644531
acceleration:6.89538461538
float64 : (1558, 1558) matmul (1558, 1558)
i:1558 use numpy:0.0313770771027
i:1558 use minpy:0.011726140976
acceleration:2.67582294695
float64 : (1559, 1559) matmul (1559, 1559)
i:1559 use numpy:0.0328531265259
i:1559 use minpy:0.00731301307678
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float64 : (1560, 1560) matmul (1560, 1560)
i:1560 use numpy:0.0301079750061
i:1560 use minpy:0.00495290756226
acceleration:6.0788485607
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i:1561 use numpy:0.0268759727478
i:1561 use minpy:0.00492095947266
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float64 : (1562, 1562) matmul (1562, 1562)
i:1562 use numpy:0.0317378044128
i:1562 use minpy:0.00512409210205
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float64 : (1563, 1563) matmul (1563, 1563)
i:1563 use numpy:0.026918888092
i:1563 use minpy:0.00489687919617
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float64 : (1564, 1564) matmul (1564, 1564)
i:1564 use numpy:0.0321679115295
i:1564 use minpy:0.0119049549103
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float64 : (1565, 1565) matmul (1565, 1565)
i:1565 use numpy:0.0352458953857
i:1565 use minpy:0.00696802139282
acceleration:5.05823581742
float64 : (1566, 1566) matmul (1566, 1566)
i:1566 use numpy:0.0272238254547
i:1566 use minpy:0.00496697425842
acceleration:5.48096769548
float64 : (1567, 1567) matmul (1567, 1567)
i:1567 use numpy:0.0348949432373
i:1567 use minpy:0.0124940872192
acceleration:2.79291657125
float64 : (1568, 1568) matmul (1568, 1568)
i:1568 use numpy:0.0343251228333
i:1568 use minpy:0.00702118873596
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float64 : (1569, 1569) matmul (1569, 1569)
i:1569 use numpy:0.0272791385651
i:1569 use minpy:0.00498914718628
acceleration:5.46769568957
float64 : (1570, 1570) matmul (1570, 1570)
i:1570 use numpy:0.0273931026459
i:1570 use minpy:0.00503587722778
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float64 : (1571, 1571) matmul (1571, 1571)
i:1571 use numpy:0.0366241931915
i:1571 use minpy:0.00552797317505
acceleration:6.62524799448
float64 : (1572, 1572) matmul (1572, 1572)
i:1572 use numpy:0.033182144165
i:1572 use minpy:0.00499987602234
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float64 : (1573, 1573) matmul (1573, 1573)
i:1573 use numpy:0.0814428329468
i:1573 use minpy:0.0121109485626
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float64 : (1574, 1574) matmul (1574, 1574)
i:1574 use numpy:0.0326750278473
i:1574 use minpy:0.00710201263428
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float64 : (1575, 1575) matmul (1575, 1575)
i:1575 use numpy:0.0281958580017
i:1575 use minpy:0.00507616996765
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float64 : (1576, 1576) matmul (1576, 1576)
i:1576 use numpy:0.0328991413116
i:1576 use minpy:0.0121209621429
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float64 : (1577, 1577) matmul (1577, 1577)
i:1577 use numpy:0.0457479953766
i:1577 use minpy:0.00814890861511
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float64 : (1578, 1578) matmul (1578, 1578)
i:1578 use numpy:0.0276420116425
i:1578 use minpy:0.00509309768677
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float64 : (1579, 1579) matmul (1579, 1579)
i:1579 use numpy:0.0328059196472
i:1579 use minpy:0.0121102333069
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float64 : (1580, 1580) matmul (1580, 1580)
i:1580 use numpy:0.0327088832855
i:1580 use minpy:0.00714588165283
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float64 : (1581, 1581) matmul (1581, 1581)
i:1581 use numpy:0.0275678634644
i:1581 use minpy:0.00506114959717
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float64 : (1582, 1582) matmul (1582, 1582)
i:1582 use numpy:0.0569689273834
i:1582 use minpy:0.0120930671692
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float64 : (1583, 1583) matmul (1583, 1583)
i:1583 use numpy:0.0332751274109
i:1583 use minpy:0.00733399391174
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float64 : (1584, 1584) matmul (1584, 1584)
i:1584 use numpy:0.0277211666107
i:1584 use minpy:0.00516200065613
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float64 : (1585, 1585) matmul (1585, 1585)
i:1585 use numpy:0.0374310016632
i:1585 use minpy:0.0123279094696
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i:1586 use numpy:0.0330312252045
i:1586 use minpy:0.00717306137085
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i:1587 use numpy:0.0279769897461
i:1587 use minpy:0.00508308410645
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i:1588 use numpy:0.032016992569
i:1588 use minpy:0.00570797920227
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float64 : (1589, 1589) matmul (1589, 1589)
i:1589 use numpy:0.0516321659088
i:1589 use minpy:0.00606107711792
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float64 : (1590, 1590) matmul (1590, 1590)
i:1590 use numpy:0.0486841201782
i:1590 use minpy:0.00579595565796
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float64 : (1591, 1591) matmul (1591, 1591)
i:1591 use numpy:0.055428981781
i:1591 use minpy:0.0137150287628
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float64 : (1592, 1592) matmul (1592, 1592)
i:1592 use numpy:0.05952501297
i:1592 use minpy:0.00775694847107
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float64 : (1593, 1593) matmul (1593, 1593)
i:1593 use numpy:0.0413801670074
i:1593 use minpy:0.00582909584045
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i:1594 use numpy:0.0355529785156
i:1594 use minpy:0.0131099224091
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float64 : (1595, 1595) matmul (1595, 1595)
i:1595 use numpy:0.0415499210358
i:1595 use minpy:0.00733304023743
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float64 : (1596, 1596) matmul (1596, 1596)
i:1596 use numpy:0.0292589664459
i:1596 use minpy:0.00518989562988
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float64 : (1597, 1597) matmul (1597, 1597)
i:1597 use numpy:0.0296900272369
i:1597 use minpy:0.00528001785278
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float64 : (1598, 1598) matmul (1598, 1598)
i:1598 use numpy:0.0341222286224
i:1598 use minpy:0.0054190158844
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float64 : (1599, 1599) matmul (1599, 1599)
i:1599 use numpy:0.0302350521088
i:1599 use minpy:0.00521612167358
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float64 : (1600, 1600) matmul (1600, 1600)
i:1600 use numpy:0.0349218845367
i:1600 use minpy:0.0124640464783
acceleration:2.8018095566
float64 : (1601, 1601) matmul (1601, 1601)
i:1601 use numpy:0.035019159317
i:1601 use minpy:0.00738000869751
acceleration:4.74513794663
float64 : (1602, 1602) matmul (1602, 1602)
i:1602 use numpy:0.0324912071228
i:1602 use minpy:0.00528407096863
acceleration:6.14889681
float64 : (1603, 1603) matmul (1603, 1603)
i:1603 use numpy:0.0365030765533
i:1603 use minpy:0.0126178264618
acceleration:2.89297658863
float64 : (1604, 1604) matmul (1604, 1604)
i:1604 use numpy:0.0352520942688
i:1604 use minpy:0.00744700431824
acceleration:4.73372818953
float64 : (1605, 1605) matmul (1605, 1605)
i:1605 use numpy:0.03156208992
i:1605 use minpy:0.00521397590637
acceleration:6.05336320819
float64 : (1606, 1606) matmul (1606, 1606)
i:1606 use numpy:0.0306808948517
i:1606 use minpy:0.00525188446045
acceleration:5.84188305793
float64 : (1607, 1607) matmul (1607, 1607)
i:1607 use numpy:0.034530878067
i:1607 use minpy:0.00542998313904
acceleration:6.3592974753
float64 : (1608, 1608) matmul (1608, 1608)
i:1608 use numpy:0.0335760116577
i:1608 use minpy:0.00548696517944
acceleration:6.11923177196
float64 : (1609, 1609) matmul (1609, 1609)
i:1609 use numpy:0.0298299789429
i:1609 use minpy:0.00525093078613
acceleration:5.68089357065
float64 : (1610, 1610) matmul (1610, 1610)
i:1610 use numpy:0.0354199409485
i:1610 use minpy:0.005539894104
acceleration:6.39361335858
float64 : (1611, 1611) matmul (1611, 1611)
i:1611 use numpy:0.031268119812
i:1611 use minpy:0.00530695915222
acceleration:5.89190889079
float64 : (1612, 1612) matmul (1612, 1612)
i:1612 use numpy:0.0358459949493
i:1612 use minpy:0.012815952301
acceleration:2.79698255014
float64 : (1613, 1613) matmul (1613, 1613)
i:1613 use numpy:0.041944026947
i:1613 use minpy:0.00744009017944
acceleration:5.63756969813
float64 : (1614, 1614) matmul (1614, 1614)
i:1614 use numpy:0.0413768291473
i:1614 use minpy:0.00533413887024
acceleration:7.75698386448
float64 : (1615, 1615) matmul (1615, 1615)
i:1615 use numpy:0.0356559753418
i:1615 use minpy:0.0126819610596
acceleration:2.81155060911
float64 : (1616, 1616) matmul (1616, 1616)
i:1616 use numpy:0.0383410453796
i:1616 use minpy:0.00752401351929
acceleration:5.09582356296
float64 : (1617, 1617) matmul (1617, 1617)
i:1617 use numpy:0.0303499698639
i:1617 use minpy:0.00543713569641
acceleration:5.58197763648
float64 : (1618, 1618) matmul (1618, 1618)
i:1618 use numpy:0.036808013916
i:1618 use minpy:0.0128390789032
acceleration:2.86687340996
float64 : (1619, 1619) matmul (1619, 1619)
i:1619 use numpy:0.0361850261688
i:1619 use minpy:0.00761580467224
acceleration:4.75130701562
float64 : (1620, 1620) matmul (1620, 1620)
i:1620 use numpy:0.0307099819183
i:1620 use minpy:0.00543189048767
acceleration:5.65364526182
float64 : (1621, 1621) matmul (1621, 1621)
i:1621 use numpy:0.0356879234314
i:1621 use minpy:0.0128610134125
acceleration:2.77489201565
float64 : (1622, 1622) matmul (1622, 1622)
i:1622 use numpy:0.0363399982452
i:1622 use minpy:0.00755286216736
acceleration:4.81142081505
float64 : (1623, 1623) matmul (1623, 1623)
i:1623 use numpy:0.0492720603943
i:1623 use minpy:0.00535607337952
acceleration:9.19928778099
float64 : (1624, 1624) matmul (1624, 1624)
i:1624 use numpy:0.0306839942932
i:1624 use minpy:0.00541400909424
acceleration:5.66751805531
float64 : (1625, 1625) matmul (1625, 1625)
i:1625 use numpy:0.0408248901367
i:1625 use minpy:0.00550603866577
acceleration:7.41456655408
float64 : (1626, 1626) matmul (1626, 1626)
i:1626 use numpy:0.0723340511322
i:1626 use minpy:0.00536489486694
acceleration:13.4828459692
float64 : (1627, 1627) matmul (1627, 1627)
i:1627 use numpy:0.0638928413391
i:1627 use minpy:0.0133020877838
acceleration:4.80321904182
float64 : (1628, 1628) matmul (1628, 1628)
i:1628 use numpy:0.0377140045166
i:1628 use minpy:0.00751185417175
acceleration:5.02059859714
float64 : (1629, 1629) matmul (1629, 1629)
i:1629 use numpy:0.0335259437561
i:1629 use minpy:0.00538802146912
acceleration:6.22231072171
float64 : (1630, 1630) matmul (1630, 1630)
i:1630 use numpy:0.0356478691101
i:1630 use minpy:0.05189204216
acceleration:0.68696215501
float64 : (1631, 1631) matmul (1631, 1631)
i:1631 use numpy:0.0521559715271
i:1631 use minpy:0.00758695602417
acceleration:6.87442649739
float64 : (1632, 1632) matmul (1632, 1632)
i:1632 use numpy:0.0306701660156
i:1632 use minpy:0.00539302825928
acceleration:5.68700265252
float64 : (1633, 1633) matmul (1633, 1633)
i:1633 use numpy:0.0330550670624
i:1633 use minpy:0.00548505783081
acceleration:6.02638442146
float64 : (1634, 1634) matmul (1634, 1634)
i:1634 use numpy:0.0348289012909
i:1634 use minpy:0.00559210777283
acceleration:6.22822425922
float64 : (1635, 1635) matmul (1635, 1635)
i:1635 use numpy:0.0303289890289
i:1635 use minpy:0.00539803504944
acceleration:5.61852391679
float64 : (1636, 1636) matmul (1636, 1636)
i:1636 use numpy:0.0474178791046
i:1636 use minpy:0.00540900230408
acceleration:8.76647419227
float64 : (1637, 1637) matmul (1637, 1637)
i:1637 use numpy:0.0414409637451
i:1637 use minpy:0.00647401809692
acceleration:6.4011195404
float64 : (1638, 1638) matmul (1638, 1638)
i:1638 use numpy:0.0301220417023
i:1638 use minpy:0.00540995597839
acceleration:5.567890353
float64 : (1639, 1639) matmul (1639, 1639)
i:1639 use numpy:0.0363080501556
i:1639 use minpy:0.00684118270874
acceleration:5.3072767826
float64 : (1640, 1640) matmul (1640, 1640)
i:1640 use numpy:0.0447800159454
i:1640 use minpy:0.00594806671143
acceleration:7.5284992785
float64 : (1641, 1641) matmul (1641, 1641)
i:1641 use numpy:0.030641078949
i:1641 use minpy:0.00545978546143
acceleration:5.61213973799
float64 : (1642, 1642) matmul (1642, 1642)
i:1642 use numpy:0.030433177948
i:1642 use minpy:0.00551319122314
acceleration:5.52006573257
float64 : (1643, 1643) matmul (1643, 1643)
i:1643 use numpy:0.0359199047089
i:1643 use minpy:0.00568604469299
acceleration:6.31720407564
float64 : (1644, 1644) matmul (1644, 1644)
i:1644 use numpy:0.0308961868286
i:1644 use minpy:0.00546908378601
acceleration:5.64924364619
float64 : (1645, 1645) matmul (1645, 1645)
i:1645 use numpy:0.0327229499817
i:1645 use minpy:0.00550103187561
acceleration:5.94851124691
float64 : (1646, 1646) matmul (1646, 1646)
i:1646 use numpy:0.0359590053558
i:1646 use minpy:0.00564002990723
acceleration:6.37567636118
float64 : (1647, 1647) matmul (1647, 1647)
i:1647 use numpy:0.0366129875183
i:1647 use minpy:0.00546002388
acceleration:6.70564604166
float64 : (1648, 1648) matmul (1648, 1648)
i:1648 use numpy:0.0310301780701
i:1648 use minpy:0.0058970451355
acceleration:5.26198754751
float64 : (1649, 1649) matmul (1649, 1649)
i:1649 use numpy:0.0362091064453
i:1649 use minpy:0.0057110786438
acceleration:6.34015195792
float64 : (1650, 1650) matmul (1650, 1650)
i:1650 use numpy:0.0312230587006
i:1650 use minpy:0.00550079345703
acceleration:5.67610090153
float64 : (1651, 1651) matmul (1651, 1651)
i:1651 use numpy:0.0322451591492
i:1651 use minpy:0.00547099113464
acceleration:5.89384233233
float64 : (1652, 1652) matmul (1652, 1652)
i:1652 use numpy:0.0364310741425
i:1652 use minpy:0.00577688217163
acceleration:6.30635575733
float64 : (1653, 1653) matmul (1653, 1653)
i:1653 use numpy:0.031594991684
i:1653 use minpy:0.00548696517944
acceleration:5.75819066655
float64 : (1654, 1654) matmul (1654, 1654)
i:1654 use numpy:0.0313291549683
i:1654 use minpy:0.00556182861328
acceleration:5.63288751715
float64 : (1655, 1655) matmul (1655, 1655)
i:1655 use numpy:0.0406010150909
i:1655 use minpy:0.00573301315308
acceleration:7.08196789487
float64 : (1656, 1656) matmul (1656, 1656)
i:1656 use numpy:0.0328149795532
i:1656 use minpy:0.00612902641296
acceleration:5.35402808574
float64 : (1657, 1657) matmul (1657, 1657)
i:1657 use numpy:0.0318720340729
i:1657 use minpy:0.00556588172913
acceleration:5.72632255301
float64 : (1658, 1658) matmul (1658, 1658)
i:1658 use numpy:0.0371580123901
i:1658 use minpy:0.0057909488678
acceleration:6.41656717032
float64 : (1659, 1659) matmul (1659, 1659)
i:1659 use numpy:0.0319678783417
i:1659 use minpy:0.00555491447449
acceleration:5.75488218378
float64 : (1660, 1660) matmul (1660, 1660)
i:1660 use numpy:0.031907081604
i:1660 use minpy:0.00562286376953
acceleration:5.67452510176
float64 : (1661, 1661) matmul (1661, 1661)
i:1661 use numpy:0.0376448631287
i:1661 use minpy:0.00580787658691
acceleration:6.48169129721
float64 : (1662, 1662) matmul (1662, 1662)
i:1662 use numpy:0.0399880409241
i:1662 use minpy:0.00565099716187
acceleration:7.07628048266
float64 : (1663, 1663) matmul (1663, 1663)
i:1663 use numpy:0.0366649627686
i:1663 use minpy:0.0206799507141
acceleration:1.77297147732
float64 : (1664, 1664) matmul (1664, 1664)
i:1664 use numpy:0.037535905838
i:1664 use minpy:0.00582003593445
acceleration:6.44942853632
float64 : (1665, 1665) matmul (1665, 1665)
i:1665 use numpy:0.0325829982758
i:1665 use minpy:0.0056619644165
acceleration:5.75471618663
float64 : (1666, 1666) matmul (1666, 1666)
i:1666 use numpy:0.038242816925
i:1666 use minpy:0.0056459903717
acceleration:6.77344706727
float64 : (1667, 1667) matmul (1667, 1667)
i:1667 use numpy:0.0386960506439
i:1667 use minpy:0.00635385513306
acceleration:6.09016885553
float64 : (1668, 1668) matmul (1668, 1668)
i:1668 use numpy:0.0327589511871
i:1668 use minpy:0.00569796562195
acceleration:5.74923636972
float64 : (1669, 1669) matmul (1669, 1669)
i:1669 use numpy:0.0330641269684
i:1669 use minpy:0.00563812255859
acceleration:5.86438599459
float64 : (1670, 1670) matmul (1670, 1670)
i:1670 use numpy:0.0378768444061
i:1670 use minpy:0.00583696365356
acceleration:6.4891348746
float64 : (1671, 1671) matmul (1671, 1671)
i:1671 use numpy:0.0332639217377
i:1671 use minpy:0.00565481185913
acceleration:5.88240998398
float64 : (1672, 1672) matmul (1672, 1672)
i:1672 use numpy:0.0326409339905
i:1672 use minpy:0.00576901435852
acceleration:5.65797412902
float64 : (1673, 1673) matmul (1673, 1673)
i:1673 use numpy:0.0478668212891
i:1673 use minpy:0.00582408905029
acceleration:8.21876535124
float64 : (1674, 1674) matmul (1674, 1674)
i:1674 use numpy:0.0327079296112
i:1674 use minpy:0.00566411018372
acceleration:5.77459275161
float64 : (1675, 1675) matmul (1675, 1675)
i:1675 use numpy:0.0328660011292
i:1675 use minpy:0.00566506385803
acceleration:5.8015235049
float64 : (1676, 1676) matmul (1676, 1676)
i:1676 use numpy:0.0394411087036
i:1676 use minpy:0.00593304634094
acceleration:6.64769941732
float64 : (1677, 1677) matmul (1677, 1677)
i:1677 use numpy:0.0373730659485
i:1677 use minpy:0.00567197799683
acceleration:6.58907103825
float64 : (1678, 1678) matmul (1678, 1678)
i:1678 use numpy:0.0330338478088
i:1678 use minpy:0.00570201873779
acceleration:5.79336009366
float64 : (1679, 1679) matmul (1679, 1679)
i:1679 use numpy:0.0395770072937
i:1679 use minpy:0.00599098205566
acceleration:6.60609678446
float64 : (1680, 1680) matmul (1680, 1680)
i:1680 use numpy:0.0331752300262
i:1680 use minpy:0.00575399398804
acceleration:5.76560039778
float64 : (1681, 1681) matmul (1681, 1681)
i:1681 use numpy:0.0348289012909
i:1681 use minpy:0.00576591491699
acceleration:6.04048130996
float64 : (1682, 1682) matmul (1682, 1682)
i:1682 use numpy:0.0386278629303
i:1682 use minpy:0.00607204437256
acceleration:6.36159101618
float64 : (1683, 1683) matmul (1683, 1683)
i:1683 use numpy:0.0343289375305
i:1683 use minpy:0.00586295127869
acceleration:5.85523158879
float64 : (1684, 1684) matmul (1684, 1684)
i:1684 use numpy:0.0337219238281
i:1684 use minpy:0.00577402114868
acceleration:5.84028408622
float64 : (1685, 1685) matmul (1685, 1685)
i:1685 use numpy:0.0391638278961
i:1685 use minpy:0.00596213340759
acceleration:6.56876074699
float64 : (1686, 1686) matmul (1686, 1686)
i:1686 use numpy:0.0373899936676
i:1686 use minpy:0.00603199005127
acceleration:6.19861660079
float64 : (1687, 1687) matmul (1687, 1687)
i:1687 use numpy:0.0348339080811
i:1687 use minpy:0.00578093528748
acceleration:6.02565265806
float64 : (1688, 1688) matmul (1688, 1688)
i:1688 use numpy:0.0395479202271
i:1688 use minpy:0.00597596168518
acceleration:6.61783363256
float64 : (1689, 1689) matmul (1689, 1689)
i:1689 use numpy:0.0515179634094
i:1689 use minpy:0.00577402114868
acceleration:8.92237178958
float64 : (1690, 1690) matmul (1690, 1690)
i:1690 use numpy:0.0348868370056
i:1690 use minpy:0.00593209266663
acceleration:5.88103372051
float64 : (1691, 1691) matmul (1691, 1691)
i:1691 use numpy:0.0442268848419
i:1691 use minpy:0.00598978996277
acceleration:7.38371213629
float64 : (1692, 1692) matmul (1692, 1692)
i:1692 use numpy:0.0399150848389
i:1692 use minpy:0.00577116012573
acceleration:6.91630174337
float64 : (1693, 1693) matmul (1693, 1693)
i:1693 use numpy:0.0690999031067
i:1693 use minpy:0.00581407546997
acceleration:11.8849339785
float64 : (1694, 1694) matmul (1694, 1694)
i:1694 use numpy:0.0408391952515
i:1694 use minpy:0.00603699684143
acceleration:6.76481971486
float64 : (1695, 1695) matmul (1695, 1695)
i:1695 use numpy:0.0378451347351
i:1695 use minpy:0.00579404830933
acceleration:6.53172578389
float64 : (1696, 1696) matmul (1696, 1696)
i:1696 use numpy:0.0398449897766
i:1696 use minpy:0.00595903396606
acceleration:6.68648475634
float64 : (1697, 1697) matmul (1697, 1697)
i:1697 use numpy:0.0436229705811
i:1697 use minpy:0.00701689720154
acceleration:6.2168461826
float64 : (1698, 1698) matmul (1698, 1698)
i:1698 use numpy:0.0345749855042
i:1698 use minpy:0.00601196289062
acceleration:5.75103109137
float64 : (1699, 1699) matmul (1699, 1699)
i:1699 use numpy:0.0360419750214
i:1699 use minpy:0.00585699081421
acceleration:6.15366767076
float64 : (1700, 1700) matmul (1700, 1700)
i:1700 use numpy:0.0403921604156
i:1700 use minpy:0.0060510635376
acceleration:6.67521670607
float64 : (1701, 1701) matmul (1701, 1701)
i:1701 use numpy:0.0348930358887
i:1701 use minpy:0.00584387779236
acceleration:5.97087022153
float64 : (1702, 1702) matmul (1702, 1702)
i:1702 use numpy:0.0352818965912
i:1702 use minpy:0.0058491230011
acceleration:6.03199771736
float64 : (1703, 1703) matmul (1703, 1703)
i:1703 use numpy:0.0414140224457
i:1703 use minpy:0.00602293014526
acceleration:6.8760589027
float64 : (1704, 1704) matmul (1704, 1704)
i:1704 use numpy:0.0429298877716
i:1704 use minpy:0.0059380531311
acceleration:7.22962338392
float64 : (1705, 1705) matmul (1705, 1705)
i:1705 use numpy:0.0383701324463
i:1705 use minpy:0.0058810710907
acceleration:6.52434426562
float64 : (1706, 1706) matmul (1706, 1706)
i:1706 use numpy:0.0410940647125
i:1706 use minpy:0.00612998008728
acceleration:6.70378437245
float64 : (1707, 1707) matmul (1707, 1707)
i:1707 use numpy:0.0363099575043
i:1707 use minpy:0.00598812103271
acceleration:6.06366459627
float64 : (1708, 1708) matmul (1708, 1708)
i:1708 use numpy:0.0356550216675
i:1708 use minpy:0.00590300559998
acceleration:6.04014701725
float64 : (1709, 1709) matmul (1709, 1709)
i:1709 use numpy:0.042160987854
i:1709 use minpy:0.006343126297
acceleration:6.64672054125
float64 : (1710, 1710) matmul (1710, 1710)
i:1710 use numpy:0.0355460643768
i:1710 use minpy:0.00603103637695
acceleration:5.89385673624
float64 : (1711, 1711) matmul (1711, 1711)
i:1711 use numpy:0.0369651317596
i:1711 use minpy:0.00602197647095
acceleration:6.13837200095
float64 : (1712, 1712) matmul (1712, 1712)
i:1712 use numpy:0.0428509712219
i:1712 use minpy:0.00610303878784
acceleration:7.02125166029
float64 : (1713, 1713) matmul (1713, 1713)
i:1713 use numpy:0.036456823349
i:1713 use minpy:0.00647306442261
acceleration:5.63208103131
float64 : (1714, 1714) matmul (1714, 1714)
i:1714 use numpy:0.0423300266266
i:1714 use minpy:0.00605201721191
acceleration:6.9943665301
float64 : (1715, 1715) matmul (1715, 1715)
i:1715 use numpy:0.042093038559
i:1715 use minpy:0.006108045578
acceleration:6.89140872009
float64 : (1716, 1716) matmul (1716, 1716)
i:1716 use numpy:0.0358648300171
i:1716 use minpy:0.00599098205566
acceleration:5.9864692773
float64 : (1717, 1717) matmul (1717, 1717)
i:1717 use numpy:0.0430269241333
i:1717 use minpy:0.00602698326111
acceleration:7.13904822184
float64 : (1718, 1718) matmul (1718, 1718)
i:1718 use numpy:0.0560147762299
i:1718 use minpy:0.00622916221619
acceleration:8.99234508363
float64 : (1719, 1719) matmul (1719, 1719)
i:1719 use numpy:0.041216135025
i:1719 use minpy:0.00655007362366
acceleration:6.29246896953
float64 : (1720, 1720) matmul (1720, 1720)
i:1720 use numpy:0.0361499786377
i:1720 use minpy:0.00610589981079
acceleration:5.92049980476
float64 : (1721, 1721) matmul (1721, 1721)
i:1721 use numpy:0.0444369316101
i:1721 use minpy:0.00619292259216
acceleration:7.17543792108
float64 : (1722, 1722) matmul (1722, 1722)
i:1722 use numpy:0.110757112503
i:1722 use minpy:0.00600385665894
acceleration:18.4476610277
float64 : (1723, 1723) matmul (1723, 1723)
i:1723 use numpy:0.0405790805817
i:1723 use minpy:0.00699615478516
acceleration:5.8001976554
float64 : (1724, 1724) matmul (1724, 1724)
i:1724 use numpy:0.0498058795929
i:1724 use minpy:0.00677990913391
acceleration:7.34609839294
float64 : (1725, 1725) matmul (1725, 1725)
i:1725 use numpy:0.0349769592285
i:1725 use minpy:0.00599980354309
acceleration:5.82968408504
float64 : (1726, 1726) matmul (1726, 1726)
i:1726 use numpy:0.0347108840942
i:1726 use minpy:0.00602698326111
acceleration:5.75924680565
float64 : (1727, 1727) matmul (1727, 1727)
i:1727 use numpy:0.0460720062256
i:1727 use minpy:0.00628399848938
acceleration:7.33163865387
float64 : (1728, 1728) matmul (1728, 1728)
i:1728 use numpy:0.0350158214569
i:1728 use minpy:0.00625991821289
acceleration:5.59365478367
float64 : (1729, 1729) matmul (1729, 1729)
i:1729 use numpy:0.0346891880035
i:1729 use minpy:0.00609397888184
acceleration:5.69237089202
float64 : (1730, 1730) matmul (1730, 1730)
i:1730 use numpy:0.0403339862823
i:1730 use minpy:0.00620007514954
acceleration:6.50540280715
float64 : (1731, 1731) matmul (1731, 1731)
i:1731 use numpy:0.0348601341248
i:1731 use minpy:0.00608086585999
acceleration:5.73275828269
float64 : (1732, 1732) matmul (1732, 1732)
i:1732 use numpy:0.0349159240723
i:1732 use minpy:0.00600504875183
acceleration:5.81442807798
float64 : (1733, 1733) matmul (1733, 1733)
i:1733 use numpy:0.0502581596375
i:1733 use minpy:0.0061948299408
acceleration:8.11291998614
float64 : (1734, 1734) matmul (1734, 1734)
i:1734 use numpy:0.0485060214996
i:1734 use minpy:0.00617909431458
acceleration:7.85002122159
float64 : (1735, 1735) matmul (1735, 1735)
i:1735 use numpy:0.0351440906525
i:1735 use minpy:0.00612115859985
acceleration:5.74141154475
float64 : (1736, 1736) matmul (1736, 1736)
i:1736 use numpy:0.0438749790192
i:1736 use minpy:0.00622701644897
acceleration:7.04590703729
float64 : (1737, 1737) matmul (1737, 1737)
i:1737 use numpy:0.0355079174042
i:1737 use minpy:0.00614213943481
acceleration:5.7810340812
float64 : (1738, 1738) matmul (1738, 1738)
i:1738 use numpy:0.0372521877289
i:1738 use minpy:0.00613403320312
acceleration:6.07303327114
float64 : (1739, 1739) matmul (1739, 1739)
i:1739 use numpy:0.0452711582184
i:1739 use minpy:0.00627303123474
acceleration:7.21679145605
float64 : (1740, 1740) matmul (1740, 1740)
i:1740 use numpy:0.043004989624
i:1740 use minpy:0.00631713867188
acceleration:6.80766908213
float64 : (1741, 1741) matmul (1741, 1741)
i:1741 use numpy:0.0448260307312
i:1741 use minpy:0.00609397888184
acceleration:7.35579029734
float64 : (1742, 1742) matmul (1742, 1742)
i:1742 use numpy:0.0664539337158
i:1742 use minpy:0.012139081955
acceleration:5.47437886674
float64 : (1743, 1743) matmul (1743, 1743)
i:1743 use numpy:0.0361218452454
i:1743 use minpy:0.00612998008728
acceleration:5.8926529501
float64 : (1744, 1744) matmul (1744, 1744)
i:1744 use numpy:0.0428991317749
i:1744 use minpy:0.0073299407959
acceleration:5.85258912308
float64 : (1745, 1745) matmul (1745, 1745)
i:1745 use numpy:0.0422780513763
i:1745 use minpy:0.0063681602478
acceleration:6.63897416698
float64 : (1746, 1746) matmul (1746, 1746)
i:1746 use numpy:0.0602841377258
i:1746 use minpy:0.0278899669647
acceleration:2.16149907248
float64 : (1747, 1747) matmul (1747, 1747)
i:1747 use numpy:0.0362319946289
i:1747 use minpy:0.00615477561951
acceleration:5.88680999419
float64 : (1748, 1748) matmul (1748, 1748)
i:1748 use numpy:0.0511338710785
i:1748 use minpy:0.00636100769043
acceleration:8.03864317841
float64 : (1749, 1749) matmul (1749, 1749)
i:1749 use numpy:0.0361380577087
i:1749 use minpy:0.0062038898468
acceleration:5.82506437108
float64 : (1750, 1750) matmul (1750, 1750)
i:1750 use numpy:0.0361731052399
i:1750 use minpy:0.00632214546204
acceleration:5.7216502621
float64 : (1751, 1751) matmul (1751, 1751)
i:1751 use numpy:0.042062997818
i:1751 use minpy:0.00645089149475
acceleration:6.52049377241
float64 : (1752, 1752) matmul (1752, 1752)
i:1752 use numpy:0.0360949039459
i:1752 use minpy:0.00617003440857
acceleration:5.85003284516
float64 : (1753, 1753) matmul (1753, 1753)
i:1753 use numpy:0.0372450351715
i:1753 use minpy:0.00632882118225
acceleration:5.88498775664
float64 : (1754, 1754) matmul (1754, 1754)
i:1754 use numpy:0.0432918071747
i:1754 use minpy:0.00646996498108
acceleration:6.69119652135
float64 : (1755, 1755) matmul (1755, 1755)
i:1755 use numpy:0.0382039546967
i:1755 use minpy:0.00626420974731
acceleration:6.09876684174
float64 : (1756, 1756) matmul (1756, 1756)
i:1756 use numpy:0.0374300479889
i:1756 use minpy:0.00627183914185
acceleration:5.96795407892
float64 : (1757, 1757) matmul (1757, 1757)
i:1757 use numpy:0.043112039566
i:1757 use minpy:0.00636601448059
acceleration:6.77221826898
float64 : (1758, 1758) matmul (1758, 1758)
i:1758 use numpy:0.0394260883331
i:1758 use minpy:0.00632286071777
acceleration:6.2354826546
float64 : (1759, 1759) matmul (1759, 1759)
i:1759 use numpy:0.0374748706818
i:1759 use minpy:0.00631308555603
acceleration:5.93606254013
float64 : (1760, 1760) matmul (1760, 1760)
i:1760 use numpy:0.043918132782
i:1760 use minpy:0.00656890869141
acceleration:6.68575783972
float64 : (1761, 1761) matmul (1761, 1761)
i:1761 use numpy:0.0377027988434
i:1761 use minpy:0.0063841342926
acceleration:5.90570265526
float64 : (1762, 1762) matmul (1762, 1762)
i:1762 use numpy:0.0380058288574
i:1762 use minpy:0.00634694099426
acceleration:5.98805454341
float64 : (1763, 1763) matmul (1763, 1763)
i:1763 use numpy:0.0436148643494
i:1763 use minpy:0.0063750743866
acceleration:6.84146751935
float64 : (1764, 1764) matmul (1764, 1764)
i:1764 use numpy:0.038074016571
i:1764 use minpy:0.0063111782074
acceleration:6.03279060104
float64 : (1765, 1765) matmul (1765, 1765)
i:1765 use numpy:0.0383648872375
i:1765 use minpy:0.0062780380249
acceleration:6.11096764393
float64 : (1766, 1766) matmul (1766, 1766)
i:1766 use numpy:0.0456171035767
i:1766 use minpy:0.00642418861389
acceleration:7.10083503433
float64 : (1767, 1767) matmul (1767, 1767)
i:1767 use numpy:0.0380699634552
i:1767 use minpy:0.00637197494507
acceleration:5.97459402829
float64 : (1768, 1768) matmul (1768, 1768)
i:1768 use numpy:0.0388569831848
i:1768 use minpy:0.00632095336914
acceleration:6.14732951116
float64 : (1769, 1769) matmul (1769, 1769)
i:1769 use numpy:0.0441780090332
i:1769 use minpy:0.0064799785614
acceleration:6.81761654218
float64 : (1770, 1770) matmul (1770, 1770)
i:1770 use numpy:0.0386080741882
i:1770 use minpy:0.00633597373962
acceleration:6.09347130762
float64 : (1771, 1771) matmul (1771, 1771)
i:1771 use numpy:0.0392289161682
i:1771 use minpy:0.00639510154724
acceleration:6.13421317526
float64 : (1772, 1772) matmul (1772, 1772)
i:1772 use numpy:0.0440540313721
i:1772 use minpy:0.00656604766846
acceleration:6.70936819172
float64 : (1773, 1773) matmul (1773, 1773)
i:1773 use numpy:0.0460920333862
i:1773 use minpy:0.00777316093445
acceleration:5.92963837684
float64 : (1774, 1774) matmul (1774, 1774)
i:1774 use numpy:0.0418789386749
i:1774 use minpy:0.00630688667297
acceleration:6.64019203871
float64 : (1775, 1775) matmul (1775, 1775)
i:1775 use numpy:0.0490958690643
i:1775 use minpy:0.00656604766846
acceleration:7.47723311547
float64 : (1776, 1776) matmul (1776, 1776)
i:1776 use numpy:0.0449800491333
i:1776 use minpy:0.00647306442261
acceleration:6.94880294659
float64 : (1777, 1777) matmul (1777, 1777)
i:1777 use numpy:0.0385718345642
i:1777 use minpy:0.00645804405212
acceleration:5.9726806217
float64 : (1778, 1778) matmul (1778, 1778)
i:1778 use numpy:0.0441918373108
i:1778 use minpy:0.00654792785645
acceleration:6.74898048354
float64 : (1779, 1779) matmul (1779, 1779)
i:1779 use numpy:0.0389969348907
i:1779 use minpy:0.00644183158875
acceleration:6.05370294978
float64 : (1780, 1780) matmul (1780, 1780)
i:1780 use numpy:0.0390419960022
i:1780 use minpy:0.00638318061829
acceleration:6.11638591118
float64 : (1781, 1781) matmul (1781, 1781)
i:1781 use numpy:0.0476520061493
i:1781 use minpy:0.00673604011536
acceleration:7.07418681202
float64 : (1782, 1782) matmul (1782, 1782)
i:1782 use numpy:0.0387148857117
i:1782 use minpy:0.0064549446106
acceleration:5.99770998005
float64 : (1783, 1783) matmul (1783, 1783)
i:1783 use numpy:0.0413398742676
i:1783 use minpy:0.00673699378967
acceleration:6.13624942492
float64 : (1784, 1784) matmul (1784, 1784)
i:1784 use numpy:0.04509806633
i:1784 use minpy:0.00674700737
acceleration:6.68415845083
float64 : (1785, 1785) matmul (1785, 1785)
i:1785 use numpy:0.0594019889832
i:1785 use minpy:0.00638890266418
acceleration:9.29768257641
float64 : (1786, 1786) matmul (1786, 1786)
i:1786 use numpy:0.0396409034729
i:1786 use minpy:0.00646090507507
acceleration:6.1355031551
float64 : (1787, 1787) matmul (1787, 1787)
i:1787 use numpy:0.0490050315857
i:1787 use minpy:0.00658798217773
acceleration:7.43854950782
float64 : (1788, 1788) matmul (1788, 1788)
i:1788 use numpy:0.0394198894501
i:1788 use minpy:0.0065279006958
acceleration:6.03867786706
float64 : (1789, 1789) matmul (1789, 1789)
i:1789 use numpy:0.0399231910706
i:1789 use minpy:0.00645613670349
acceleration:6.18375863215
float64 : (1790, 1790) matmul (1790, 1790)
i:1790 use numpy:0.0459921360016
i:1790 use minpy:0.00668001174927
acceleration:6.88503818974
float64 : (1791, 1791) matmul (1791, 1791)
i:1791 use numpy:0.0396990776062
i:1791 use minpy:0.00651907920837
acceleration:6.08967560253
float64 : (1792, 1792) matmul (1792, 1792)
i:1792 use numpy:0.0410439968109
i:1792 use minpy:0.00654220581055
acceleration:6.2737244898
float64 : (1793, 1793) matmul (1793, 1793)
i:1793 use numpy:0.04660820961
i:1793 use minpy:0.00670218467712
acceleration:6.95418163708
float64 : (1794, 1794) matmul (1794, 1794)
i:1794 use numpy:0.040403842926
i:1794 use minpy:0.00650691986084
acceleration:6.2093653818
float64 : (1795, 1795) matmul (1795, 1795)
i:1795 use numpy:0.0402460098267
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acceleration:6.22984942427
float64 : (1796, 1796) matmul (1796, 1796)
i:1796 use numpy:0.047639131546
i:1796 use minpy:0.00703310966492
acceleration:6.77355164582
float64 : (1797, 1797) matmul (1797, 1797)
i:1797 use numpy:0.0405647754669
i:1797 use minpy:0.00715899467468
acceleration:5.66626702634
float64 : (1798, 1798) matmul (1798, 1798)
i:1798 use numpy:0.0437371730804
i:1798 use minpy:0.00655698776245
acceleration:6.67031488619
float64 : (1799, 1799) matmul (1799, 1799)
i:1799 use numpy:0.0525329113007
i:1799 use minpy:0.00664210319519
acceleration:7.90907785635
float64 : (1800, 1800) matmul (1800, 1800)
i:1800 use numpy:0.0692911148071
i:1800 use minpy:0.00716209411621
acceleration:9.67470039947
float64 : (1801, 1801) matmul (1801, 1801)
i:1801 use numpy:0.0411839485168
i:1801 use minpy:0.00644993782043
acceleration:6.38516985177
float64 : (1802, 1802) matmul (1802, 1802)
i:1802 use numpy:0.0476639270782
i:1802 use minpy:0.00668597221375
acceleration:7.12894483472
float64 : (1803, 1803) matmul (1803, 1803)
i:1803 use numpy:0.0420558452606
i:1803 use minpy:0.00662994384766
acceleration:6.34331846951
float64 : (1804, 1804) matmul (1804, 1804)
i:1804 use numpy:0.0412690639496
i:1804 use minpy:0.00649976730347
acceleration:6.34931406353
float64 : (1805, 1805) matmul (1805, 1805)
i:1805 use numpy:0.0481860637665
i:1805 use minpy:0.00678014755249
acceleration:7.10693438357
float64 : (1806, 1806) matmul (1806, 1806)
i:1806 use numpy:0.0415308475494
i:1806 use minpy:0.00663805007935
acceleration:6.25648301128
float64 : (1807, 1807) matmul (1807, 1807)
i:1807 use numpy:0.0417430400848
i:1807 use minpy:0.00671315193176
acceleration:6.21809851902
float64 : (1808, 1808) matmul (1808, 1808)
i:1808 use numpy:0.0474419593811
i:1808 use minpy:0.00681805610657
acceleration:6.95828233731
float64 : (1809, 1809) matmul (1809, 1809)
i:1809 use numpy:0.0429780483246
i:1809 use minpy:0.00652599334717
acceleration:6.58567148911
float64 : (1810, 1810) matmul (1810, 1810)
i:1810 use numpy:0.0414290428162
i:1810 use minpy:0.0066351890564
acceleration:6.24383758534
float64 : (1811, 1811) matmul (1811, 1811)
i:1811 use numpy:0.105635166168
i:1811 use minpy:0.00774383544922
acceleration:13.6411945813
float64 : (1812, 1812) matmul (1812, 1812)
i:1812 use numpy:0.0434668064117
i:1812 use minpy:0.00669813156128
acceleration:6.4893927529
float64 : (1813, 1813) matmul (1813, 1813)
i:1813 use numpy:0.0425519943237
i:1813 use minpy:0.00688886642456
acceleration:6.17692254447
float64 : (1814, 1814) matmul (1814, 1814)
i:1814 use numpy:0.0489439964294
i:1814 use minpy:0.00688195228577
acceleration:7.11193486922
float64 : (1815, 1815) matmul (1815, 1815)
i:1815 use numpy:0.0440740585327
i:1815 use minpy:0.00685787200928
acceleration:6.42678347935
float64 : (1816, 1816) matmul (1816, 1816)
i:1816 use numpy:0.0413329601288
i:1816 use minpy:0.00667405128479
acceleration:6.19308398528
float64 : (1817, 1817) matmul (1817, 1817)
i:1817 use numpy:0.0566780567169
i:1817 use minpy:0.0076310634613
acceleration:7.42728153216
float64 : (1818, 1818) matmul (1818, 1818)
i:1818 use numpy:0.0436642169952
i:1818 use minpy:0.00670909881592
acceleration:6.50820895522
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i:1819 use numpy:0.0413310527802
i:1819 use minpy:0.00666189193726
acceleration:6.2041013528
float64 : (1820, 1820) matmul (1820, 1820)
i:1820 use numpy:0.0477669239044
i:1820 use minpy:0.00810980796814
acceleration:5.89001910922
float64 : (1821, 1821) matmul (1821, 1821)
i:1821 use numpy:0.0449199676514
i:1821 use minpy:0.00670123100281
acceleration:6.70324118547
float64 : (1822, 1822) matmul (1822, 1822)
i:1822 use numpy:0.0621290206909
i:1822 use minpy:0.0233550071716
acceleration:2.66020131077
float64 : (1823, 1823) matmul (1823, 1823)
i:1823 use numpy:0.0520181655884
i:1823 use minpy:0.0069899559021
acceleration:7.44184460059
float64 : (1824, 1824) matmul (1824, 1824)
i:1824 use numpy:0.0437920093536
i:1824 use minpy:0.00683307647705
acceleration:6.40882763433
float64 : (1825, 1825) matmul (1825, 1825)
i:1825 use numpy:0.0420758724213
i:1825 use minpy:0.00665998458862
acceleration:6.31771318107
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i:1826 use numpy:0.0479559898376
i:1826 use minpy:0.00687599182129
acceleration:6.97441054092
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i:1827 use numpy:0.0415940284729
i:1827 use minpy:0.00674486160278
acceleration:6.16677271121
float64 : (1828, 1828) matmul (1828, 1828)
i:1828 use numpy:0.0418651103973
i:1828 use minpy:0.0068678855896
acceleration:6.09577865722
float64 : (1829, 1829) matmul (1829, 1829)
i:1829 use numpy:0.048033952713
i:1829 use minpy:0.00796103477478
acceleration:6.033