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ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°è³‡æ–™ - Supercomputing Programing Contest Official Site
2024-08-19 MenuBar 2024-08-05 Supercomputing Contest News News/sc240731 2024-07-31 SupercomputingContest2024 2024-07-03 News/sc240703 SupercomputingContest2024/予é¸çµæžœ 2024-06-26 News/sc240626 2024-06-20 SupercomputingContest2024/予é¸ãƒ»èªå®šå•é¡Œï¼±ï¼†ï¼¡ 2024-06-05 News/sc240605 News/sc240605-2 2023-09-13 SupercomputingContest2023 2023-09-06 News/sc230906 2023-09-01 News/sc230830 2023-08-28 News/sc230828 Supercomp
GitHub - NVIDIA/cccl: CUDA Core Compute 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
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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 12.6.3 (November 2024), Versioned Online Documentation Archive
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
CUDA Training Series – Oak Ridge Leadership Computing Facility
CUDA® is a parallel computing platform and programming model that extends C++ to allow developers to program GPUs with a familiar programming language and simple APIs. NVIDIA will present a 13-part CUDA training series intended to help new and existing GPU programmers understand the main concepts of the CUDA platform and its programming model. Each part will include a 1-hour presentation and examp
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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
1. Why CUDA Compatibility — CUDA Compatibility r555 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
Performance Evaluation of Advanced Features in CUDA Unified Memory
cupy/time_range.py at master · cupy/cupy · GitHub
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