background
In the article about the problems encountered in compiling and installing LitmusRT , we have compiled and installed the real-time operating system LitmusRT, and can start it normally. Now, we have to compile and install the GPU accelerated third-party library OpenCL or OpenACC.
Here again, be careful not to install the Nvidia driver with a virtual machine, because the graphics card of the virtual machine is virtualized, and Nvidia's ko file cannot be loaded. So I am using the ubuntu16.04 64-bit desktop computer in the laboratory, which has been installed with NVIDIA drivers, cuda10.2 and 10.1, gcc7, and g++7.
OpenCL
If there is cuda, Nvidia is the first choice. After all, Nvidia is the leader in the GPU industry. But if we have to use a virtual machine, we have to switch to the Intel version of OpenCL to install
Nvidia version
1. Download the installation package http://developer.download.nvidia.com/compute/cuda/4_0/sdk/gpucomputingsdk_4.0.17_linux.run
2. Before running the run file, make sure that your gcc and g++ are both version 3.4, if not, install and switch
3. Drag the run file to the server and run it
root@sundata:/data/szc# ./gpucomputingsdk_4.0.17_linux.runWill let you specify the installation directory and the cuda directory, just press Enter to default
3. Then copy some library files to the /usr/lib or /usr/local/include directory
root@sundata:/data/szc# cp -r /usr/local/cuda-10.0/extras/CUPTI/include/* /usr/local/include
root@sundata:/data/szc# cp -r /snap/gnome-3-34-1804/36/usr/include/* /usr/local/include
root@sundata:/data/szc# cp /usr/local/cuda-10.0/lib64/libOpenCL.so.1.1 /usr/local/lib/libOpenCL.so
root@sundata:/data/szc# cp /usr/lib/x86_64-linux-gnu/libGLU.so.1.3.1 /usr/lib/libGLU.so
root@sundata:/data/szc# cp /snap/gnome-3-34-1804/60/usr/lib/x86_64-linux-gnu/libGL.so.1.0.0 /usr/lib/libGL.so
root@sundata:/data/szc# cp /snap/gnome-3-34-1804/60/usr/lib/x86_64-linux-gnu/libX11.so.6.3.0 /usr/lib/libX11.so
root@sundata:/data/szc# cp /usr/lib/x86_64-linux-gnu/libXmu.so.6.2.0 /usr/lib/libXmu.soThe names and location positioning methods of these things are as follows:
First cut into the OpenCL installation directory, and then make
root@sundata:~/NVIDIA_GPU_Computing_SDK/OpenCL# makeIt will report an error that it cannot find a certain header file or a certain so library (cannot find -lxxx), similar to this
Then locate the header file or xxx library, you will get the path of the library, then copy the header file to the /usr/loca/include directory, and copy the library file to /usr/local/lib.
I can’t find glut.so here, so I have to install this dependency
root@sundata:~/NVIDIA_GPU_Computing_SDK/OpenCL# apt-get install freeglut3-devIf you encounter this error:
ld cannot find crt1.o: No such file or directoryIt is necessary to set the environment variable, the following path is the result of locate crt1.o
export LIBRARY_PATH=$LIBRARY_PATH:/snap/gnome-3-34-1804/36/usr/lib/x86_64-linux-gnu/4. Finally, go to the OpenCL installation directory and perform make
root@sundata:~/NVIDIA_GPU_Computing_SDK/OpenCL# makeYou will see a bunch of executable files in the bin/linux/release/ directory, execute one
Or see if the OpenCL SDK can be detected
See that the SDK and the devices that support it can be detected, and that's it
ps: During the compilation process, I also encountered problems such as ibstdc++.so.6 error adding symbols: DSO missing from command line, Nonrepresentable section on output, etc. After every attempt, I re-run the run file by reducing the gcc and g++ version to 3.4 Installed and resolved. In fact, these link problems were encountered after I dropped gcc and g++. How can I try the methods on the Internet? How can I change the common/common_opencl.mk file? Finally, I re-executed the run file and the problem was solved.
5. If we want to write our own opencl file, we have to copy all the header files in cuda to the /usr/local/include directory
root@sundata:/data/szc# cp -r /usr/local/cuda-10.0/include/* /usr/local/include/Then write sample code
#include <stdio.h>
#include <stdlib.h>
#include <CL/cl.h>
// OpenCL source code
const char* OpenCLSource[] = {
"__kernel void VectorAdd(__global int* c, __global int* a,__global int* b)",
"{",
" // Index of the elements to add \n",
" unsigned int n = get_global_id(0);",
" // Sum the n’th element of vectors a and b and store in c \n",
" c[n] = a[n] + b[n];",
"}"
};
// Some interesting data for the vectors
int InitialData1[20] = {37,50,54,50,56,0,43,43,74,71,32,36,16,43,56,100,50,25,15,17};
int InitialData2[20] = {35,51,54,58,55,32,36,69,27,39,35,40,16,44,55,14,58,75,18,15};
// Number of elements in the vectors to be added
#define SIZE 2048
int main(int argc, char **argv) {
// Two integer source vectors in Host memory
int HostVector1[SIZE], HostVector2[SIZE];
// Initialize with some interesting repeating data
int c;
for(c = 0; c < SIZE; c++) {
HostVector1[c] = InitialData1[c%20];
HostVector2[c] = InitialData2[c%20];
}
//Get an OpenCL platform
cl_platform_id cpPlatform;
clGetPlatformIDs(1, &cpPlatform, NULL);
// Get a GPU device
cl_device_id cdDevice;
clGetDeviceIDs(cpPlatform, CL_DEVICE_TYPE_GPU, 1, &cdDevice, NULL);
// Create a context to run OpenCL on our CUDA-enabled NVIDIA GPU
cl_context GPUContext = clCreateContext(0, 1, &cdDevice, NULL, NULL, NULL);
// Create a command-queue on the GPU device
cl_command_queue cqCommandQueue = clCreateCommandQueue(GPUContext, cdDevice, 0, NULL);
// Allocate GPU memory for source vectors AND initialize from CPU memory
cl_mem GPUVector1 = clCreateBuffer(GPUContext, CL_MEM_READ_ONLY |
CL_MEM_COPY_HOST_PTR, sizeof(int) * SIZE, HostVector1, NULL);
cl_mem GPUVector2 = clCreateBuffer(GPUContext, CL_MEM_READ_ONLY |
CL_MEM_COPY_HOST_PTR, sizeof(int) * SIZE, HostVector2, NULL);
// Allocate output memory on GPU
cl_mem GPUOutputVector = clCreateBuffer(GPUContext, CL_MEM_WRITE_ONLY, sizeof(int) * SIZE, NULL, NULL);
// Create OpenCL program with source code
cl_program OpenCLProgram = clCreateProgramWithSource(GPUContext, 7, OpenCLSource, NULL, NULL);
// Build the program (OpenCL JIT compilation)
clBuildProgram(OpenCLProgram, 0, NULL, NULL, NULL, NULL);
// Create a handle to the compiled OpenCL function (Kernel)
cl_kernel OpenCLVectorAdd = clCreateKernel(OpenCLProgram, "VectorAdd", NULL);
// In the next step we associate the GPU memory with the Kernel arguments
clSetKernelArg(OpenCLVectorAdd, 0, sizeof(cl_mem),(void*)&GPUOutputVector);
clSetKernelArg(OpenCLVectorAdd, 1, sizeof(cl_mem), (void*)&GPUVector1);
clSetKernelArg(OpenCLVectorAdd, 2, sizeof(cl_mem), (void*)&GPUVector2);
// Launch the Kernel on the GPU
size_t WorkSize[1] = {SIZE}; // one dimensional Range
clEnqueueNDRangeKernel(cqCommandQueue, OpenCLVectorAdd, 1, NULL, WorkSize, NULL, 0, NULL, NULL);
// Copy the output in GPU memory back to CPU memory
int HostOutputVector[SIZE];
clEnqueueReadBuffer(cqCommandQueue, GPUOutputVector, CL_TRUE, 0,
SIZE * sizeof(int), HostOutputVector, 0, NULL, NULL);
// Cleanup
clReleaseKernel(OpenCLVectorAdd);
clReleaseProgram(OpenCLProgram);
clReleaseCommandQueue(cqCommandQueue);
clReleaseContext(GPUContext);
clReleaseMemObject(GPUVector1);
clReleaseMemObject(GPUVector2);
clReleaseMemObject(GPUOutputVector);
// Print out the results
int Rows;
for (Rows = 0; Rows < (SIZE/20); Rows++, printf("\t")) {
for(c = 0; c <20; c++) {
printf("%c",(char)HostOutputVector[Rows * 20 + c]);
}
}
printf("\n\nThe End\n\n");
return 0;
}Compile
root@sundata:/data/szc# gcc test_opencl.c -o test_opencl -lOpenCLrun
ps: In the official website manual, the context is created
cl_context GPUContext = clCreateContextFromType(0, CL_DEVICE_TYPE_GPU, NULL, NULL, NULL);But this function will run into an error, which can be checked by passing the error variable pointer into the last parameter
....
cl_int ciErr1;
cl_context GPUContext = clCreateContextFromType(0, CL_DEVICE_TYPE_GPU, NULL, NULL, &ciErr1);
if (ciErr1 != CL_SUCCESS) {
printf("Error in clCreateContext, error: %d\n", ciErr1);
return -1;
}The results are as follows
So you have to use
cl_context GPUContext = clCreateContext(0, 1, &cdDevice, NULL, NULL, NULL);To create the context
Intel version
1. Download dependencies
(base) root@ubuntu:/home/szc# apt-get install clinfo
(base) root@ubuntu:/home/szc# apt install dkms xz-utils openssl libnuma1 libpciaccess0 bc curl libssl-dev lsb-core libicu-dev
(base) root@ubuntu:/home/szc# echo "deb http://download.mono-project.com/repo/debian wheezy main" | sudo tee /etc/apt/sources.list.d/mono-xamarin.list
(base) root@ubuntu:/home/szc# apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 3FA7E0328081BFF6A14DA29AA6A19B38D3D831EF
(base) root@ubuntu:/home/szc# apt-get update
(base) root@ubuntu:/home/szc# apt-get install mono-complete2. Download the Intel opensl sdk source code http://registrationcenter-download.intel.com/akdlm/irc_nas/vcp/16284/intel_sdk_for_opencl_applications_2020.0.270.tar.gz , upload it to ubuntu, unzip it, and enter its directory
(base) root@ubuntu:/home/szc# tar -zxvf intel_sdk_for_opencl_applications_2020.0.270.tar.gz
(base) root@ubuntu:/home/szc# cd intel_sdk_for_opencl_applications_2020.0.2703. Then execute the installation script
(base) root@ubuntu:/home/szc/intel_sdk_for_opencl_applications_2020.0.270# ./install.sh4. The default is all the way. After the completion, check whether the installation is complete, and you can see Intel(R) CPU Runtime for OpenCL(TM) Applications.
(base) root@ubuntu:/home/szc/intel_sdk_for_opencl_applications_2020.0.270# clinfo
Number of platforms 1
Platform Name Intel(R) CPU Runtime for OpenCL(TM) Applications
Platform Vendor Intel(R) Corporation
Platform Version OpenCL 2.1 LINUX
Platform Profile FULL_PROFILE
Platform Extensions cl_khr_icd cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_byte_addressable_store cl_khr_depth_images cl_khr_3d_image_writes cl_intel_exec_by_local_thread cl_khr_spir cl_khr_fp64 cl_khr_image2d_from_buffer cl_intel_vec_len_hint
Platform Host timer resolution 1ns
Platform Extensions function suffix INTEL
Platform Name Intel(R) CPU Runtime for OpenCL(TM) Applications
Number of devices 1
Device Name Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz
Device Vendor Intel(R) Corporation
Device Vendor ID 0x8086
Device Version OpenCL 2.1 (Build 0)
Driver Version 18.1.0.0920
Device OpenCL C Version OpenCL C 2.0
Device Type CPU
Device Profile FULL_PROFILE
Max compute units 4
Max clock frequency 2200MHz
Device Partition (core)
Max number of sub-devices 4
Supported partition types by counts, equally, by names (Intel)
Max work item dimensions 3
Max work item sizes 8192x8192x8192
Max work group size 8192
Preferred work group size multiple 128
Max sub-groups per work group 1
Preferred / native vector sizes
char 1 / 32
short 1 / 16
int 1 / 8
long 1 / 4
half 0 / 0 (n/a)
float 1 / 8
double 1 / 4 (cl_khr_fp64)
Half-precision Floating-point support (n/a)
Single-precision Floating-point support (core)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero No
Round to infinity No
IEEE754-2008 fused multiply-add No
Support is emulated in software No
Correctly-rounded divide and sqrt operations No
Double-precision Floating-point support (cl_khr_fp64)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Correctly-rounded divide and sqrt operations No
Address bits 64, Little-Endian
Global memory size 6233903104 (5.806GiB)
Error Correction support No
Max memory allocation 1558475776 (1.451GiB)
Unified memory for Host and Device Yes
Shared Virtual Memory (SVM) capabilities (core)
Coarse-grained buffer sharing Yes
Fine-grained buffer sharing Yes
Fine-grained system sharing Yes
Atomics Yes
Minimum alignment for any data type 128 bytes
Alignment of base address 1024 bits (128 bytes)
Preferred alignment for atomics
SVM 64 bytes
Global 64 bytes
Local 0 bytes
Max size for global variable 65536 (64KiB)
Preferred total size of global vars 65536 (64KiB)
Global Memory cache type Read/Write
Global Memory cache size 262144
Global Memory cache line 64 bytes
Image support Yes
Max number of samplers per kernel 480
Max size for 1D images from buffer 97404736 pixels
Max 1D or 2D image array size 2048 images
Base address alignment for 2D image buffers 64 bytes
Pitch alignment for 2D image buffers 64 bytes
Max 2D image size 16384x16384 pixels
Max 3D image size 2048x2048x2048 pixels
Max number of read image args 480
Max number of write image args 480
Max number of read/write image args 480
Max number of pipe args 16
Max active pipe reservations 65535
Max pipe packet size 1024
Local memory type Global
Local memory size 32768 (32KiB)
Max constant buffer size 131072 (128KiB)
Max number of constant args 480
Max size of kernel argument 3840 (3.75KiB)
Queue properties (on host)
Out-of-order execution Yes
Profiling Yes
Local thread execution (Intel) Yes
Queue properties (on device)
Out-of-order execution Yes
Profiling Yes
Preferred size 4294967295 (4GiB)
Max size 4294967295 (4GiB)
Max queues on device 4294967295
Max events on device 4294967295
Prefer user sync for interop No
Profiling timer resolution 1ns
Execution capabilities
Run OpenCL kernels Yes
Run native kernels Yes
Sub-group independent forward progress No
IL version SPIR-V_1.0
SPIR versions 1.2
printf() buffer size 1048576 (1024KiB)
Built-in kernels
Device Available Yes
Compiler Available Yes
Linker Available Yes
Device Extensions cl_khr_icd cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_byte_addressable_store cl_khr_depth_images cl_khr_3d_image_writes cl_intel_exec_by_local_thread cl_khr_spir cl_khr_fp64 cl_khr_image2d_from_buffer cl_intel_vec_len_hint
NULL platform behavior
clGetPlatformInfo(NULL, CL_PLATFORM_NAME, ...) No platform
clGetDeviceIDs(NULL, CL_DEVICE_TYPE_ALL, ...) No platform
clCreateContext(NULL, ...) [default] No platform
clCreateContext(NULL, ...) [other] Success [INTEL]
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CPU) No platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_GPU) No platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ACCELERATOR) No platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CUSTOM) No platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ALL) No platformOpenACC
Environment: ubuntu server, equipped with Nvidia driver and cuda, with network cable plugged in
1. Download the compressed package and unzip it
root@sundata:/data/szc# wget https://developer.download.nvidia.com/hpc-sdk/20.7/nvhpc_2020_207_Linux_x86_64_cuda_multi.tar.gz
root@sundata:/data/szc# tar xpzf nvhpc_2020_207_Linux_x86_64_cuda_multi.tar.gz2. Installation
root@sundata:/data/szc# nvhpc_2020_207_Linux_x86_64_cuda_multi/installYou will be required to configure parameters during installation, just select the single system and your own installation path
3. Test
First set the environment variables, change /root/NVIDIA_GPU_Computing_SDK/hpc_sdk to your own installation path
root@sundata:/data/szc# export PATH=/root/NVIDIA_GPU_Computing_SDK/hpc_sdk/Linux_x86_64/2020/compilers/bin/:$PATHThen switch to a test sample directory under the installation path, compile and run
root@sundata:/data/szc# cd ~/NVIDIA_GPU_Computing_SDK/hpc_sdk/Linux_x86_64/2020/examples/OpenMP
root@sundata:~/NVIDIA_GPU_Computing_SDK/hpc_sdk/Linux_x86_64/2020/examples/OpenMP# make NTHREADS=4 matmul_testRun screenshot
Finally, attach the official manual: https://docs.nvidia.com/hpc-sdk/archive/20.7/index.html
Conclusion
These files are not small. If downloading from the official website is slow, you can download it from my Baidu network disk:
Intel OpenCL: Link: https://pan.baidu.com/s/1a9_H5tbsfFjdMmPFlJbUzg, extraction code: 060s
NVIDIA OpenCL: Link: https://pan.baidu.com/s/1J_qrL-PREONvIYnz1F7DoQ, extraction code: c2ci
NVIDIA OpenACC: Link: https://pan.baidu.com/s/1hQKKtrq4c6TEfXMuE_RC5w, extraction code: 9u7o