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The video explains how GPUs are well-suited for parallel computing, making them ideal for neural network programming. NVIDIA created the CUDA software platform to accelerate computations using its specialized GPU hardware, making it easier for developers to build software and optimize computations using parallel processing power. The video shows how easy it is to use CUDA with PyTorch and notes the emergence of GP GPU, a new programming model for scientific computing tasks using parallel programming techniques. However, the video acknowledges that not all computational tasks are suitable for GPUs, and bottleneck issues may slow down performance in certain cases.
In this section, we learn about the use of GPUs for neural network programming and how they are well-suited for parallel computing. GPUs are processors specialized in handling complex computations at high speeds. Tasks most suited for GPUs are those that can be executed in parallel, with CPUs being suitable for handling general computations. Neural networks are embarrassing parallel, meaning that computations can be done independently of one another. NVIDIA created CUDA – software platform – to accelerate computations using their GPU hardware, making it easier for developers to build software and thereby optimize computations using parallel processing power.
In this section, the video explains the role of an NVIDIA GPU and CUDA in deep learning. NVIDIA GPU enables parallel computations while CUDA is the software layer that provides an API for developers to develop specialized libraries for high-performance computing. CUDA comes with specialized libraries like KUDI and the CUDA deep learning neural network library for developers to use. The video shows how easy it is to use CUDA with pi torch by calling the CUDA function on data structures. However, the video acknowledges that not all computational tasks are suitable for GPUs and bottleneck issues, such as moving data from CPU to GPU, may slow down overall performance in certain cases.
In this section, the video explains that the task of training neural networks for deep learning is one of the more recent and emerging varieties of parallel tasks for GPUs. This, along with other scientific computing tasks that use parallel programming techniques, is leading to a new programming model called GP GPU or general-purpose GPU computing. The video notes that NVIDIA has been a pioneer in this space, and CUDA, which NVIDIA created nearly 10 years ago, is now really taking flight. The video discusses the GPU computing stack, which includes the hardware on the bottom, the software architecture (CUDA) on top of the GPU, and libraries like KU DN in on top of CUDA, with PyTorch being the framework being worked with.
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