GitHub - NVIDIA/cuda-samples: Samples for CUDA Developers which demonstrates features in CUDA ToolkitYou signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
Supercomputing Contest 2013/GPUプログラミング資料 - Supercomputing Programing Contest Official Site
2023-09-13 SupercomputingContest2023 2023-09-06 Supercomputing Contest News News/sc230906 2023-09-01 News/sc230830 2023-08-28 News/sc230828 SupercomputingContest2023/本選結果 2023-08-01 MenuBar 2023-06-29 News/sc230629 SupercomputingContest2023/予選結果 2023-06-12 SupercomputingContest2023/予選・認定問題Ｑ＆Ａ 2023-06-01 SupercomputingContest2023/問題および関連ファイル群更新履歴 2023-05-31 News/sc230531 2023-05-30 News/sc230530 20
GitHub - NVIDIA/cccl: CUDA C++ Core Libraries
The concept for the CUDA C++ Core Libraries (CCCL) grew organically out of the Thrust, CUB, and libcudacxx projects that were developed independently over the years with a similar goal: to provide high-quality, high-performance, and easy-to-use C++ abstractions for CUDA developers. Naturally, there was a lot of overlap among the three projects, and it became clear the community would be better ser
How Nvidia’s CUDA Monopoly In Machine Learning Is Breaking - OpenAI Triton And PyTorch 2.0
The 1,000-foot summary is that the default software stack for machine learning models will no longer be Nvidia’s closed-source CUDA. The ball was in Nvidia’s court, and they let OpenAI and Meta take control of the software stack. That ecosystem built its own tools because of Nvidia’s failure with their proprietary tools, and now Nvidia’s moat will be permanently weakened. TensorFlow vs. PyTorch A
CUDAの高速化の復習2023年版 Histogram（主にatomicAdd）編 - まったり勉強ノート
2月 19, 2023 / 最終更新日時 : 2月 19, 2023 Shuji Suzuki (shu) プログラミング Reduction、vectrized memory accessに続き、今回はhistogramを題材にして主にatomicAddのパフォーマンスが最近どうなっているのかを見ていきたいと思います。 HistogramはCUDA Samplesの中にもありますが、全然違う実装が、NVIDIAのA100の最適化に関する発表の資料の中で紹介されています。この資料ではatomicAddとL2キャッシュの「persistent data accesses」を利用してhistogramの実装をしています。このpersistent data accessesは発表当時気になっていて、あとで調べようと思って忘れてたのですが、最近調べたのでせっかくなので記事にしました。 今回調査する
CUDA Toolkit Archive
Previous releases of the CUDA Toolkit, GPU Computing SDK, documentation and developer drivers can be found using the links below. Please select the release you want from the list below, and be sure to check www.nvidia.com/drivers for more recent production drivers appropriate for your hardware configuration. Latest Release CUDA Toolkit 12.3.2 (January 2024), Versioned Online Documentation Archived
CUDA C/C++ Basics
© NVIDIA Corporation 2011 CUDA C/C++ Basics Supercomputing 2011 Tutorial Cyril Zeller, NVIDIA Corporation © NVIDIA Corporation 2011 What is CUDA? CUDA Architecture Expose GPU computing for general purpose Retain performance CUDA C/C++ Based on industry-standard C/C++ Small set of extensions to enable heterogeneous programming Straightforward APIs to manage devices, memory etc. This
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Bonus Lecture: CUDA C++ llm.cpp
Slide 1
Unifying the CUDA C++ Core Libraries: Towards a More Delightful CUDA C++ · NVIDIA/cccl · Discussion #520
CUDA Toolkit 12.2: New Accelerated Computing and Security Enhancements Revealed
CUDA Toolkit 11.7 Update 1 Downloads
Index of /compute/cuda/11.7.1
NVIDIA Datacenter Drivers :: NVIDIA Data Center GPU Driver Documentation
CUDA Compatibility :: NVIDIA GPU Management and Deployment Documentation
TensorFlow Release Notes :: Deep Learning Frameworks Documentation
GitHub - avidday/hpl-cuda: simple port of hpl-2.0 to use NVIDIA GPU accelation with CUBLAS
CUDA Training Series
Performance Evaluation of Advanced Features in CUDA Unified Memory
cupy/time_range.py at master · cupy/cupy · GitHub