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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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/codinginavan/profiling-a-cuda-python-program-with-gpuflight-466d |
| Publication time | Fri, 22 May 2026 05:35:35 +0000 |
| Retrieval time | 2026-05-22T06:02:00.658Z |
| Last seen | 2026-05-22T06:02:00.658Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | gTKGRcqXpHkN |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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