Introduction to CUDA, cuDNN and Pytorch

foreword

Before explaining cuda and cuDNN, let's first understand NVIDIA (NVIDA).

NVIDIA is a leading global computer technology company focused on graphics processing units (GPUs) and artificial intelligence (AI) computing. The company was founded in 1993 and is headquartered in Santa Clara, California, USA. NVIDIA's products and technologies are widely used in various fields, including games, virtual reality, autonomous driving, data centers, edge computing, etc.

As one of the world's most well-known GPU manufacturers, NVIDIA's graphics processor technology has promoted the development of computer graphics and the gaming industry. At the same time, due to its advantages in parallel computing capabilities, NVIDIA's GPUs are also widely used in the fields of scientific computing, deep learning and artificial intelligence. Its flagship GPU lineup includes GeForce for gamers, Quadro for professional workstations and data scientists, Tesla for high-performance computing, and more.

NVIDIA also launched a series of software development tools and libraries, providing developers with rich tools and support to accelerate the development and deployment process of artificial intelligence applications. The most famous of these is the CUDA platform, which provides developers with programming models and tools for high-performance computing on GPUs.

Through continuous innovation and technology leadership, NVIDIA has achieved great success in the computer industry and made important contributions to the development of artificial intelligence and high-performance computing.

1. CUDA

Official website address: https://developer.nvidia.com/cuda-toolkit

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA for general-purpose computing (GPGPU) using NVIDIA GPU (Graphics Processing Unit). It is a software environment that provides high performance and ease of use for GPU programming.

The main goal of CUDA is to use GPU as a computing acceleration device for performing parallel computing tasks, especially in the fields of scientific computing and deep learning. It provides a set of programming interfaces (APIs) and tool sets that enable developers to take advantage of the massively parallel computing capabilities of GPUs to accelerate computationally intensive tasks.

Using CUDA, developers can use programming languages ​​such as C/C++, Python, etc. to write GPU-accelerated programs. CUDA provides a series of libraries and tools, such as CUDA Runtime library, CUDA Tools (such as nvcc compiler) and NVIDIA Nsight development environment for compiling, debugging and optimizing CUDA programs.

The advantage of CUDA is that it closely combines the architectural features of NVIDIA GPU, which can perform fine-grained parallel processing on tasks, and utilize hundreds to thousands of cores on the GPU to perform computing tasks simultaneously. This makes CUDA a GPU programming platform widely used in scientific computing, numerical simulation, deep learning and other fields.

It is worth noting that for a program developed using CUDA, the hardware requirement for its execution needs to be a CUDA-supporting NVIDIA GPU, and the corresponding CUDA driver and runtime library need to be installed.

2. cuDNN

Official website address: https://developer.nvidia.com/cudnn

cuDNN (CUDA Deep Neural Network) is a deep neural network (DNN) acceleration library developed by NVIDIA, which is specially used to accelerate deep learning tasks on the CUDA platform.

cuDNN provides highly optimized DNN (deep neural network) basic operations and algorithm implementations, such as convolution, pooling, normalization, activation functions, etc., as well as automatic derivation and tensor operations. It leverages the parallel computing capabilities and highly programmable architecture of NVIDIA GPUs to provide high-performance DNN computing and training acceleration.

By using cuDNN, deep learning frameworks (such as TensorFlow, PyTorch, etc.) can take advantage of the GPU acceleration provided by it to speed up training and inference. The cuDNN library implements efficient convolution calculations and other operations, optimizing the calculation process and memory usage to maximize GPU utilization and performance.

cuDNN also provides some advanced functions, such as automatic adjustment of algorithm performance and memory usage, mixed-precision calculation, etc., to further improve the efficiency and performance of deep learning tasks.

In a word, cuDNN is an important tool provided by NVIDIA for deep learning developers. It implements highly optimized DNN operations and algorithms, enabling deep learning frameworks to more effectively utilize the performance of CUDA and NVIDIA GPUs and accelerate deep learning tasks. implement.

Its main features are as follows:

• Implemented Tensor Core acceleration for various common convolutions, including 2D convolution, 3D convolution, grouped convolution, depthwise separable convolution, and dilated convolution with NHWC and NCHW inputs and outputs
• Optimized kernels for many computer vision and speech models, including ResNet, ResNext, EfficientNet, EfficientDet, SSD, MaskRCNN, Unet, VNet, BERT, GPT-2, Tacotron2, and WaveGlow
• Supports FP32, FP16, BF16 and TF32 floating point formats and INT8 and UINT8 integer formats
• Arbitrary dimensional ordering, striding and subregions of 4D tensors means easy integration into arbitrary neural network implementations
• It can speed up fusion operations on various CNN architectures

Note: cuDNN is supported on Windows and Linux systems with Ampere, Turing, Volta, Pascal, Maxwell, and Kepler GPU architectures in datacenter and mobile GPUs.

3. Pytorch

Official website address: https://pytorch.org/

PyTorch is an open source machine learning framework based on Python, focusing on deep learning tasks. It is developed and maintained by Facebook's artificial intelligence research team, and provides a wealth of tools and interfaces, making it easier and more flexible to develop and experiment with deep learning tasks in the Python environment.

PyTorch is known for its dynamic computational graph feature, which means that developers can define and adjust computational graphs in a manner similar to standard Python programming, without having to write static graphs beforehand. This makes PyTorch flexible and intuitive for easy debugging and iterative model design.

PyTorch provides a rich set of features and components, including:

1. Powerful tensor operations: PyTorch provides a tensor operation interface similar to NumPy, and has acceleration functions on the GPU.

2. Automatic derivation: PyTorch's automatic derivation function allows developers to easily calculate the gradient of tensor operations and use it for backpropagation and model optimization.

3. Efficient neural network modules: PyTorch provides modules for building neural network models, such as various layers, loss functions, etc.

4. Multiple optimizers: PyTorch supports the implementation of various optimization algorithms, such as stochastic gradient descent (SGD), Adam, etc.

5. Training and model saving: PyTorch provides a convenient training and verification interface, and supports saving and loading the trained model.

PyTorch's ecosystem is very active, with a large number of community contributions, providing a wealth of pre-trained models and extended libraries to facilitate developers to perform various deep learning tasks. At the same time, due to its ease of use and flexibility, PyTorch has been widely used and adopted in both academia and industry.

⭐️ Friends who want to do in-depth learning can refer to the following tutorial to configure the corresponding environment.
⭐️The latest version of Anaconda environment configuration, Cuda, cuDNN and pytorch environment one-click configuration process