Profiling a CUDA Python Program with GPUFlight
The article discusses profiling a CUDA Python program using GPUFlight. It focuses on a simple Numba matrix multiplication kernel to demonstrate how GPUFlight can help identify performance optimizations. The author provides a step-by-step guide on setting up the environment and running the profiling tool.
- ▪The author typically writes CUDA code in C++, but has recently been using Python with libraries like PyTorch and Numba.
- ▪Numba allows users to write GPU kernels directly in Python and compile them to GPU machine code.
- ▪GPUFlight can profile Python GPU programs and provides insights into kernel performance and optimization opportunities.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3788007) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Myoungho Shin Posted on May 22 Profiling a CUDA Python Program with GPUFlight #performance #python #tooling #tutorial In the previous post, I used a C++ CUDA example to look at memory coalescing and how memory access patterns affect GPU performance. This time, I wanted to look at a similar performance problem from Python. I usually write CUDA code in C++, but recently I have been spending more time with Python, especially PyTorch and Numba.
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