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Python Getting Started

3 ラボ · 2 クレジット · 1時 54分

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To get a basic understanding the main approaches to GPU Compute programming using Python

計算の高速化入門

CUDAライブラリの使い方、OpenACCのようなディレクティブの使い方、CUDA対応言語を用いて直接コードの書く方法というGPU上のコード移植の3つのテクニックを学びます。45分間、GPUへのポーティングと高速化に関するいくつかの演習を行います。

Icon  intro Introductory 無料 45 分

Accelerating Applications with GPU-Accelerated Libraries in Python

Learn how to accelerate your Python application using GPU drop-in libraries to harness the massively parallel power of NVIDIA GPUs. In less than an hour, you will work through three exercises, including:

  • Use a Python profiler to determine which part of the code is consuming the most amount of time
  • Use a cuRAND API call to optimize this portion of code
  • Profile again and use the CUDA Runtime API to optimize data movement to achieve more application speed-up

Please read instructions below before starting lab!

Icon  intro Introductory 1クレジット 50 分

Accelerating Applications with CUDA Python

Learn how to accelerate your Python application using CUDA to harness the massively parallel power of NVIDIA GPUs. In less than an hour, you will work through three exercises, including:

  • Hello Parallelism!
  • Accelerate the simple SAXPY algorithm
  • Accelerate a basic Matrix Multiply algorithm with CUDA

Icon  intro Introductory 1クレジット 50 分