High-Performance GPU Computing in the Julia Programming Language | NVIDIA Technical Blog
The Julia programming language is well-suited for GPU computing thanks to the CUDAnative.jl package, which enables native GPU programming and generates efficient PTX code.CUDAnative.jl reuses the Julia compiler, avoiding the need for a custom toolchain and keeping the hardware support package small, with only 1300 lines of code.The performance of Julia for GPU computing is comparable to statically
Hands-on: Turbo charge your MacBook Pro graphics performance with the Akitio Node eGPU over Thunderbolt 3 [Video] - 9to5Mac
The Node features a cover that slides on to the unit a little awkwardly, but it does a good job of protecting the internals and providing airflow to the internals. Both the front and rear of the Node feature perforated panels to allow plenty of cool air to work its way throughout the case. With a graphics card inside, the Node is pretty heavy, almost like a small PC case. The unit’s handle is obvi
![Hands-on: Turbo charge your MacBook Pro graphics performance with the Akitio Node eGPU over Thunderbolt 3 [Video] - 9to5Mac](https://cdn-ak-scissors.b.st-hatena.com/image/square/52b9241911d6b4425b61da8f1d97f85360a1439a/height=288;version=1;width=512/https%3A%2F%2F9to5mac.com%2Fwp-content%2Fuploads%2Fsites%2F6%2F2017%2F01%2Fakitio-node-rx-480-gpu.jpg%3Fquality%3D82%26strip%3Dall)
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æ–°åž‹MacBook Proã«å¯¾å¿œã—ãŸå¤–付ã‘GPU「BizonBOX 3ã€ãŒç™»å ´ | ゴリミー
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RTX 6000Ada 㨠RTX PRO 6000 Blackwell Max-Qã®ç”»åƒç”Ÿæˆã«ãŠã‘る推論速度を比較
NVIDIA RTX 6000Ada 㨠RTX PRO 6000 Blackwell Max-Qを用ã„ã¦ã€ ç”»åƒç”Ÿæˆã«ãŠã‘る推論速度を比較 NVIDIAã®Blackwell世代ã§ã¯ã€4ビット浮動å°æ•°ç‚¹ï¼ˆFP4）精度ã®é‡å化対応ãŒæ£å¼ã«ã‚µãƒãƒ¼ãƒˆã•ã‚Œã€å¾“æ¥ã‚ˆã‚Šã‚‚ã¯ã‚‹ã‹ã«ä½Žã„精度ã§ã®é«˜é€Ÿã‹ã¤åŠ¹çŽ‡çš„ãªæŽ¨è«–ãŒå¯èƒ½ã¨ãªã‚Šã¾ã—ãŸã€‚ ã“ã‚Œã«ã‚ˆã‚Šã€ç‰¹ã«ç”»åƒç”Ÿæˆã‚¿ã‚¹ã‚¯ã«ãŠã‘る処ç†é€Ÿåº¦ã¨ç²¾åº¦ãŒè‘—ã—ãå‘上ã—ã¾ã—ãŸã€‚ システムè¦ä»¶ ベンãƒãƒžãƒ¼ã‚¯æ©Ÿç¨®ï¼šDeepLearningBOXâ…¢ GPU：GPUã¯ãã‚Œãžã‚Œ1基 CPU：1x Intel Xeon w7-3565X マザーボード：ASUS-PRO/WS/W790E-SAGE/SE (Intel W790 ãƒãƒƒãƒ—セット) MEM：128GB DDR5-4800 ECC ストレージ：M.2 NVMe SSD 1TB é›»æºï¼šHX1500i × 2 NVIDIA RTX
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Enabling GPU Computing in the R Statistical Environment R is the most popular open source statistical environment in the biomedical research community. However, most of the popular R function implementations involve no parallelism and they can only be executed as separate instances on multicore or cluster hardware for large data-parallel analysis tasks. The arrival of modern graphics processing un
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Back in 2002 I was doing some programming gigs when I started in 3D by making interactive demos of industrial motors using HTML, Flash and 3D. The job paid badly but it was really fun. I learned a ton about the creative process and  the demos meant a lot to the client as they could visualize what they were going to make before they decided to make it. It was empowering for everyone. After that job
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Build Image Classification service with Amazon ECS and GPU instances
GPUCC - An Open-Source GPGPU Compiler
pcpratts/rootbeer1 · GitHub
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