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.
DEV.to (Top) files mainly under programming. We currently carry 4,877 of its stories.
Opening excerpt (first ~120 words) tap to expand
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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
