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https://nsstudy.connpass.com/event/42602/ 「ディープラーニングã¨GPUコンピューティングã€ï¼†ã€Œã‚¹ãƒ—ラトゥーン画åƒè§£æžã€ #NSStudy 9 対ãƒãƒ³ãŒ NVIDIA ã•ã‚“ã§ã—ãŸã€‚Read less
Home — TensorFlow
Deploy ML on mobile, microcontrollers and other edge devices
How GPUs Work
UVA Graphics Paper Index How GPUs Work David Luebke Greg Humphreys IEEE Computer, 2007 Abstract In the early 1990s, ubiquitous interactive 3D graphics was still the stuff of science fiction. By the end of the decade, nearly every new computer contained a graphics processing unit (GPU) dedicated to providing a high-performance, visually rich, interactive 3D experience. This dramatic shift was the
(JP) GPGPUãŒPostgreSQLã‚’åŠ é€Ÿã™ã‚‹
1. GPGPUãŒPostgreSQLã‚’åŠ é€Ÿã™ã‚‹ ～“超â€ãƒ¡ãƒ‹ãƒ¼ã‚³ã‚¢ã«ã‚ˆã‚‹æ¤œç´¢é«˜é€ŸåŒ–～ NEC OSS推進センター The PG-Strom Project 海外 浩平 <kaigai@ak.jp.nec.com> (Tw: @kkaigai) 2. 自己紹介 â–Œåå‰: 海外 浩平 ▌所属： NEC OSS推進センター ▌好ããªã‚‚ã®ï¼š コアã®å¤šã„プãƒã‚»ãƒƒã‚µ ▌嫌ã„ãªã‚‚ã®ï¼š コアã®å°‘ãªã„プãƒã‚»ãƒƒã‚µ ▌経æ´: ï¬HPC  OSS/Linux  SAP  GPU/PostgreSQL â–ŒTw: @kkaigai ▌主ãªä»•äº‹ ï¬SELinux周り諸々 (2004~) •Lockless AVCã€JFFS2 XATTRãªã© ï¬PostgreSQL周り諸々 (2008~) •SE-PostgreSQLã€Security Barrier Viewã€Writable FDWãªã© ï¬PG-Strom
Metalã®ã€Œshared CPU/GPU memory bufferã€ã«ã¤ã„㦠: DSAS開発者ã®éƒ¨å±‹
iOS8ã®ãƒªãƒªãƒ¼ã‚¹ã«ã‚ˆã‚Šã€A7ã‚’æ載ã—ãŸiOS端末ã‹ã‚‰ã¯OpenGLESã«ä»£ã‚る新グラフィックスAPIã§ã‚ã‚‹MetalãŒå‹•ãよã†ã«ãªã‚Šã¾ã—ãŸã€‚ iOS8発表時ã®Appleã®Keynoteã§ç´¹ä»‹ã•ã‚ŒãŸã¨ãŠã‚Šã€Metalã¯OpenGLã¨ãらã¹ã¦APIã®å±¤ãŒè–„ãã¦æœ€é©åŒ–ã•ã‚Œã¦ã„ã‚‹ã®ã§é«˜é€Ÿã«å‹•ä½œã™ã‚‹ã‚ˆã†ã§ã€ä»–ã®å¤šãã®è¨˜äº‹ã§ã‚‚ã“ã®äº‹ãŒæ›¸ã‹ã‚Œã¦ã„ã¾ã™ã€‚ ã—ã‹ã—実際ã«Metalã«è§¦ã‚Œã¦ã¿ã‚‹ã¨ã€å˜ã«Appleã®ãƒãƒ¼ãƒ‰ã‚¦ã‚§ã‚¢ã«æœ€é©åŒ–ã•ã‚Œã¦ã„ã¦ã‚ªãƒ¼ãƒãƒ¼ãƒ˜ãƒƒãƒ‰ãŒä½Žã速ã„ã¨ã„ã†ã“ã¨ã«ç•™ã¾ã‚‰ãšã€ã‚る一ã¤ã®ç‰¹é•·ã«æ°—付ãã¾ã™ã€‚ ãã‚Œã¯ã€Œshared CPU/GPU memory bufferã€ã¤ã¾ã‚ŠCPU/GPUé–“ã§ãƒ¡ãƒ¢ãƒªãŒå…±æœ‰ã•ã‚Œã¦ã„ã‚‹ã¨ã„ã†ã‚‚ã®ã§ã™ã€‚ ã“ã“ã§ã¯ä»Šã¾ã§iOSã®ï¼“Dアプリケーション開発ã«åˆ©ç”¨ã•ã‚Œã¦ã„ãŸOpenGLESã§ã®ãƒ¡ãƒ¢ãƒªã®æ‰±ã„æ–¹ã¨æ¯”較ã—ã¤ã¤ã€CPU/GPUé–“ã§ãƒ¡ãƒ¢ãƒªãŒå…±æœ‰ã•ã‚Œã‚‹ã“ã¨ã®ãƒ¡ãƒªãƒƒãƒˆã«ã¤ã„
NVIDIA cuDNN | NVIDIA Developer
Accelerated Learning cuDNN provides kernels, targeting Tensor Cores, to deliver best available performance on compute-bound operations. It offers heuristics for choosing the right kernel for a given problem size. Expressive Op Graph API The user defines computations as a graph of operations on tensors. The cuDNN library has both a direct C API and an open-source C++ frontend for convenience. Most
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ã¾ãšGPU(グラフィックãƒãƒƒãƒ—)ã®æ§‹é€ ã®èª¬æ˜Žã‹ã‚‰ã€‚ パソコンã®GPUã¯BGA部å“ã§ã™ã€‚ 多ãã®CPUãŒã‚½ã‚±ãƒƒãƒˆã«æŒ¿å…¥ã™ã‚‹ã‚¿ã‚¤ãƒ—ã®PGAã¨ã„ã†ãƒãƒƒãƒ—ã§ã™ãŒã€ GPUã¯åŸºæ¿ã«ç›´æŽ¥åŠç”°ä»˜ã‘ã•ã‚Œã¦ã„ã‚‹BGAã¨ã„ã†ã‚¿ã‚¤ãƒ—ã®ãƒãƒƒãƒ—ã§ã™ã€‚ GPUã«ã¯ã€åŸºæ¿ã¨åŒã˜ç´ æã§å‡ºæ¥ã¦ã„る基æã«ã€ 「ダイã€ã¨ã„ã†åŠå°Žä½“ãƒãƒƒãƒ—ãŒè¼‰ã£ã¦ã„ã¾ã™ã€‚ ã“ã®ãƒ€ã‚¤ãŒç”»åƒå‡¦ç†ã‚’ã—ã¦ã„る部分ã§ã™ã€‚ ãã®å‡¦ç†ã‚’åŠç”°ãƒœãƒ¼ãƒ«ã‚’通ã˜ã¦åŸºæ¿ã¨ã‚„ã‚Šã¨ã‚Šã—ã¦ã„ã¾ã™ã€‚
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GPU を使用ã—ãŸä¸¦åˆ—計算環境ãŒä¸€èˆ¬çš„ã«ãªã‚Šã¤ã¤ã‚ã‚Šã¾ã™ã€‚ユーザサイド立ã£ã¦ã¿ã‚Œã°ã€ã€Œä¸¦åˆ—化é©ç”¨æ–¹æ³•ã®ç¨®é¡žã¨ãã®åŠ¹æžœã€ã‚’ã‚‚ã†ä¸€åº¦æ•´ç†ã—ã¦è€ƒãˆã€è‡ªåˆ†ã«åˆã£ãŸæ‰‹æ³•ã‚’é¸æŠžã—ãªã‘ã‚Œã°ã„ã‘ãªã„ã¨æ€ã£ã¦ã„る方も多ã„ã“ã¨ã§ã—ょã†ã€‚並列計算ã¨è¨€ã£ã¦ã‚‚ã€ãƒ—ãƒã‚»ãƒƒã‚µãƒ»ã‚³ã‚¢è‡ªä½“ãŒè‡ªå‹•çš„ã«SIMD（ベクトル処ç†ï¼‰å®Ÿè¡Œã™ã‚‹ã‚‚ã®ã‹ã‚‰ã€ãƒžãƒ«ãƒã‚³ã‚¢ã‚’利用ã—ãŸè‡ªå‹•ä¸¦åˆ—ã€OpenMPã«ã‚ˆã‚‹ã‚¹ãƒ¬ãƒƒãƒ‰ãƒ¬ãƒ™ãƒ«ã®ä¸¦åˆ—実行ã€ã•ã‚‰ã«ãƒ—ãƒã‚°ãƒ©ãƒ ã‚’æ”¹é€ ã—ã¦ãƒžãƒ«ãƒãƒ—ãƒã‚»ã‚¹ã«ã‚ˆã‚‹ MPI 並列実行ã¨æ§˜ã€…ãªæ‰‹æ³•ãŒç”¨æ„ã•ã‚Œã¦ã„ã¾ã™ã€‚ã•ã‚‰ã«ã€ä»Šã€GPUを利用ã—㟠many cores 並列を利用ã—ãŸä¸¦åˆ—å®Ÿè¡Œã‚‚åŠ ã‚ã‚Šã€ãƒ¦ãƒ¼ã‚¶ã«ã¨ã£ã¦ã¯ã€è‡ªåˆ†ã§ã‚‚手軽ã«ã§ãる方法ã®åˆ¤æ–ã‚„ãã®æ€§èƒ½åŠ¹æžœã«é–¢ã™ã‚‹ä¸€ã¤ã®åˆ¤æ–基準をæŒã£ã¦ãŠãã“ã¨ãŒå¿…è¦ã§ã—ょã†ã€‚ã¾ãŸã€ç°¡å˜ãªä¸¦åˆ—実行ã®åŽŸç†ç‰ã®çŸ¥è˜ãŒã‚ã‚‹ã ã‘ã§ã‚‚ã€ä»Šå¾Œã® many cores 並列時代ã«å‘ã‘ã¦ã€åˆç†çš„ãªä¸¦åˆ—化ã®ãŸã‚ã®æŒ‡
g2インスタンスを使ã£ã¦å®‡å®™ã¸è¡Œã“ã†ï¼ - j3tm0t0ã®æ—¥è¨˜
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AMD，CPUã¨GPUã®æ·±ã„連æºã‚’å¯èƒ½ã«ã™ã‚‹æ–°è¦ç´ 「hQã€ç™ºè¡¨ã€‚HSAã®å§¿ãŒè¦‹ãˆã¦ããŸï¼Ÿ ライター：米田 è¡ æ—¥æœ¬æ™‚é–“2013å¹´10月22æ—¥13：01，AMDã¯ï¼ŒåŒç¤¾ãŒæŽ¨é€²ã™ã‚‹ã€ŒHSAã€ï¼ˆHeterogeneous System Architecture）ã®æ–°è¦ç´ 「hQã€ã‚’発表ã—ãŸã€‚AMDã¯4月30æ—¥ã«ï¼ŒHSAã§å¯¾å¿œã™ã‚‹æ–°æŠ€è¡“ã¨ã—ã¦ã€ŒhUMAã€ï¼ˆHeterogeneous Unified Memory Architecture）ã®æ¦‚è¦ã‚’発表ã—ã¦ã„ã‚‹ãŒï¼ˆé–¢é€£è¨˜äº‹ï¼‰ï¼Œä»Šå›žã®hQã¯ãã‚Œã«ç¶šãã‚‚ã®ã¨ãªã‚‹ã€‚ 今回4Gamerã§ã¯ï¼Œã‚¢ã‚¸ã‚¢å¤ªå¹³æ´‹åœ°åŸŸã®å ±é“関係者を対象ã¨ã—ãŸäº‹å‰é›»è©±ä¼šè°ã«å‚åŠ ã§ããŸã®ã§ï¼Œãã®å†…容をè¸ã¾ãˆã¤ã¤ï¼Œã€ŒhQã¨ã¯ä½•ã‹ã€ã‚’紹介ã—ã¦ã¿ãŸã„。 「異種混åˆã€ã‚’真ã®æ„味ã§å®Ÿç¾ã™ã‚‹ãŸã‚ã®hQ㯠ユーザーモードã‹ã‚‰GPUスレッドをディスパッãƒã™ã‚‹ hQã¯ã€ŒHeterogeneous Queuei
NVIDIA，Vsync有効ã§ã‚‚無効ã§ã‚‚ãªã„第3ã®ãƒ‡ã‚£ã‚¹ãƒ—レイåŒæœŸæŠ€è¡“「G-SYNCã€ç™ºè¡¨ã€‚ãã®æ£ä½“ã¨ç‹™ã„を明らã‹ã«ã™ã‚‹
NVIDIA，Vsync有効ã§ã‚‚無効ã§ã‚‚ãªã„第3ã®ãƒ‡ã‚£ã‚¹ãƒ—レイåŒæœŸæŠ€è¡“「G-SYNCã€ç™ºè¡¨ã€‚ãã®æ£ä½“ã¨ç‹™ã„を明らã‹ã«ã™ã‚‹ ライター：西å·å–„å¸ åŒ—ç±³æ™‚é–“2013å¹´10月18日，NVIDIAã¯ï¼Œã‚«ãƒŠãƒ€ã®ãƒ¢ãƒ³ãƒˆãƒªã‚ªãƒ¼ãƒ«å¸‚ã§å ±é“関係者å‘ã‘イベントを開催。ãã®å ´ã§ï¼ŒGPUã¨å˜ä½“ディスプレイã®åŒæœŸæŠ€è¡“「G-SYNCã€ï¼ˆã‚¸ãƒ¼ã‚·ãƒ³ã‚¯ï¼‰ã‚’発表ã—ãŸã€‚ G-SYNCモジュールを掲ã’ã‚‹Jen-Hsun Huangæ°ï¼ˆPresident and CEO, NVIDIA） ç°¡å˜ã«è¨€ã†ã¨ï¼Œã“ã‚Œã¯ãƒ‡ã‚£ã‚¹ãƒ—レイデãƒã‚¤ã‚¹ã®ãƒªãƒ•ãƒ¬ãƒƒã‚·ãƒ¥ãƒ¬ãƒ¼ãƒˆã‚’å‹•çš„ã«å¤‰æ›´ã™ã‚‹æŠ€è¡“。GPUå´ã®ãƒ¬ãƒ³ãƒ€ãƒªãƒ³ã‚°ã‚·ã‚¹ãƒ†ãƒ ã§å®Ÿç¾ã™ã‚‹ã‚‚ã®ã§ã¯ãªã，ディスプレイデãƒã‚¤ã‚¹å´ã«ç‰¹åˆ¥ãªãƒãƒ¼ãƒ‰ã‚¦ã‚§ã‚¢ã‚’æ載ã™ã‚‹ã“ã¨ã§å®Ÿç¾ã™ã‚‹ã‚‚ã®ã§ã‚る。Keplerアーã‚テクãƒãƒ£ã®ç™ºè¡¨ã«åˆã‚ã›ã‚‹å½¢ã§NVIDIAã¯2012å¹´3月ã«ï¼Œå‹•çš„ã«Vsync（垂直åŒæœŸï¼‰ã®æœ‰åŠ¹/無効を切り替ãˆã‚‹ã€ŒAd
GpuTest - Cross-Platform GPU Stress Test and OpenGL Benchmark for Windows, Linux and OS X | Geeks3D.com
GpuTest is a cross-platform (Windows, Linux and Max OS X) GPU stress test and OpenGL benchmark. GpuTest comes with several GPU tests including some popular ones from Windows'world (FurMark or TessMark). GpuTest can be downloaded from THIS PAGE. The number of GPU tests grows with the new versions of the tool. The foolowing tests are available in the latest version: FurMark based stress test (OpenGL
Modern GPU
© 2013, NVIDIA CORPORATION. All rights reserved. Code and text by Sean Baxter, NVIDIA Research. (Click here for license. Click here for contact information.) Modern GPU is code and commentary intended to promote new and productive ways of thinking about GPU computing. This project is a library, an algorithms book, a tutorial, and a best-practices guide. If you are new to CUDA, start here. If you'r
GPUを用ã„ãŸSSLリãƒãƒ¼ã‚¹ãƒ—ãƒã‚ã‚·ã®å®Ÿè£…ã«ã¤ã„㦠- ゆã†ã†ãブãƒã‚°
近年，汎用計算ã®é«˜é€ŸåŒ–ã®ãŸã‚ã®ã‚¢ã‚¯ã‚»ãƒ©ãƒ¬ãƒ¼ã‚¿ã¨ã—ã¦æ³¨ç›®ã•ã‚Œã¦ã„ã‚‹GPUを，ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯å‡¦ç†ã«é©ç”¨ã™ã‚‹ä¸€ç’°ã¨ã—ã¦ï¼Œã‚µãƒ¼ãƒã‚µã‚¤ãƒ‰ã®SSL処ç†ã«æ³¨ç›®ã—ãŸè«–文をèªã‚“ã ã®ã§ï¼Œå†…容を軽ã紹介ã—ã¾ã™ï¼Ž SSLShader - GPU-accelerated SSL Proxy SSLShader SSLShader: Cheap SSL acceleration with commodity processors Proceedings of the 8th USENIX conference on Networked systems design and implementation 2011 ãªãŠï¼Œè©•ä¾¡ã«ä½¿ã‚ã‚ŒãŸå®Ÿè£…ã®ä¸€éƒ¨ã®ã‚½ãƒ¼ã‚¹ã‚³ãƒ¼ãƒ‰ãŒå…¬é–‹ã•ã‚Œã¦ã„ã¾ã™ï¼Ž http://shader.kaist.edu/sslshader/libgpucrypto/ 紹介 背景 SSL(Secure Socket
Introduction to High-Performance Computing with Rã‚’èžã„ã¦ã㟠- yasuhisa's blog
Rユーザー会ã®è‹±èªžã§ã®ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«è¬›æ¼”ã‚’èžã„ã¦ãã¾ã—ãŸã€‚åˆç«‹å·ã§ã—ãŸã€‚çµ±æ•°ç ”ãŒ10月ã«ç«‹å·ã«ç§»è»¢ã—ãŸã¨ã„ã†ã“ã¨ã§ã€ã‚¿ã‚¯ã‚·ãƒ¼ã®é‹è»¢æ‰‹ã®äººã‚‚è¦å¯Ÿå®˜ã‚‚ã€Œçµ±æ•°ç ”ã€ã¨ã„ã†è¨€è‘‰ã«ã½ã‹ã‚“ã¨ã•ã‚Œã¦ã—ã¾ã„ã¾ã—ãŸã€‚12/5ã®ã»ã†ã«å‚åŠ ã•ã‚Œã‚‹æ–¹ã¯æ™‚é–“ã«ã‹ãªã‚Šä½™è£•ã‚’æŒã£ã¦è¡Œã£ãŸã»ã†ãŒã‚ˆã„ã¨æ€ã„ã¾ã™ã€‚ ãã‚“ãªã‚ã‘ã§è‹±èªžã®ç™ºè¡¨ã‚’5時間ãらã„èžã„ã¦ã„ãŸã®ã§å¤§åˆ†ç–²ã‚Œã¾ã—ãŸãŒã€ã¨ã¦ã‚‚ãŸã‚ã«ãªã‚Šã¾ã—ãŸã€‚個人的ã«çŸ¥ã£ã¦ã„ã‚‹ã“ã¨ãŒå‰²ã‚Šã¨ã‚ã£ãŸã®ã§ã™ãŒã€çŸ¥ã‚‰ãªã„パッケージや関数ã¨ã‹ã‚‚çµæ§‹ã‚ã‚Šã¾ã—ãŸã€‚最後ã®"Extending memory limits using packages biglm, ff and bigmemory"ã¯å…¨ç„¶çŸ¥ã‚‰ãªã„ã¨ã“ã‚ã ã£ãŸã®ã§ã€ãŠãƒ¼ãŠãƒ¼è¨€ã„ãªãŒã‚‰èžã„ã¦ãŠã‚Šã¾ã—ãŸã€‚ スライドã®è‹±èªžã¯èªã‚ã‚‹&Rã¯åˆ†ã‹ã‚‹ã‘ã©ã€è‹±èªžã¯ã‹ãªã‚Šèžãå–ã‚Œãªã‹ã£ãŸ(自分ã®èƒ½åŠ›çš„ã«)ã®ã§ã‹ãªã‚Šã‚„ã°ã„ãªãƒ¼ã¨ã€‚。。 ä¼šå ´ã«é›»æºç¢ºä¿ã§
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