Beating cuBLAS in Single-Precision General Matrix Multiplication

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This blog post focuses on an SGEMM implementation that outperforms cuBLAS with its (modified?) CUTLASS kernel across a wide range of matrix sizes. The blog delves into benchmarking code on CUDA devices and explains the algorithm's design along with optimization techniques. These include inlined PTX, asynchronous memory copies, double-buffering, avoiding shared memory bank conflicts, and efficient coalesced storage using shared memory. The code is super easy to tweak, so you can customize it for your projects with kernel fusion or just drop it into your libraries as-is. If you have any questions, feel free to comment or send me a direct message - I'd love to hear your feedback and answer any questions you may have!

Visit product View on Hacker NewsJan 15, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, code · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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