AI tasks require significant computational power, and GPUs are crucial for speeding up deep learning processes. However, we often observe that GPUs are not fully utilized and deliver suboptimal performance. To address this issue, it is important to comprehend GPU architecture and learn how to optimize deep learning jobs for improved efficiency. This article focuses on providing insights into CUDA programming, a parallel computing platform developed by NVIDIA, which enables efficient utilization of GPUs in AI tasks. By understanding the fundamentals of GPU usage and optimizing deep learning workflows, users can enhance their AI job performance significantly.
GPU Architecture
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GPUs are designed to perform highly parallel computations, making them well-suited for tasks such as deep learning and computer graphics. They are made up of a large number of processing cores, each of which can execute a single instruction at a time. These cores are connected by a high-speed interconnect, allowing them to communicate with each other and coordinate their work.
CUDA Programming
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CUDA + PyTorch
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DL Profiling
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