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yamaz id:yamaz

HPCã«é–¢ã™ã‚‹yamazã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (88)

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å·¨å¤§csvã®æ•´å½¢ã‚’é«˜é€Ÿã«è¡Œã†ä¾¿åˆ©ãƒ‘ãƒƒã‚±ãƒ¼ã‚¸: NYSOL(MCMD) - Qiita
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yamaz 2017/08/20
hpc
ãƒªãƒ³ã‚¯
NYSOL biz
æ±äº¬ã‚ªãƒ•ã‚£ã‚¹ã€€ã€’104-0045 æ±äº¬éƒ½ä¸å¤®åŒºç¯‰åœ°2-7-10 ç¯‰åœ°ã‚·ãƒ†ã‚£ãƒ—ãƒ©ã‚¶7F TELï¼š03-6228-4932 FAXï¼š03-6228-4953 â€‹ å¤§é˜ªã‚ªãƒ•ã‚£ã‚¹ã€€ã€’550-0002 å¤§é˜ªå¸‚è¥¿åŒºæ±Ÿæˆ¸å €1-18-35 è‚¥å¾Œæ©‹IPãƒ“ãƒ«6F TELï¼š06-6131-6711Â FAXï¼š06-6131-6657
yamaz 2017/08/20
hpc
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ãªãœH2Oã‚’ä½œã‚‹ã®ã‹ ã€œHTTP/2ã®æœªè¸æ€§ã€œ
yamaz 2016/03/28
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person

hpc
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-æ–°è£½å“- SUPERMICRO H8QG6+-F
yamaz 2011/12/25
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hpc

diy
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GlusterFS æŠ€è¡“ã¨å‹•å‘ 1of2
1. ç¬¬4å›žã‚¯ãƒ©ã‚¦ãƒ‰ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ç ”ç©¶ä¼š GlusterFS æŠ€è¡“ã¨å‹•å‘ 1of2 2011/12/8 (æ ª)NTT PCã‚³ãƒŸãƒ¥ãƒ‹ã‚±ãƒ¼ã‚·ãƒ§ãƒ³ã‚º Copyright Â ï¼ˆCï¼‰ Â 2011, Â NTT PC Â Communications, Â Inc. Â All Â Rights Â Reserved. Â 1 2. ç™ºè¡¨è€…ã®ç´¹ä»‹ é«˜æ©‹ã€€æ•¬ç¥ (TAKAHASHI Keisuke) (å€‹äººç”¨)Twitter ID : @keithseahus â€¢â€¯ NTT PCã‚³ãƒŸãƒ¥ãƒ‹ã‚±ãƒ¼ã‚·ãƒ§ãƒ³ã‚ºåœ¨ç± (2006å¹´ã€œ) â€“â€¯Webãƒžã‚¤ãƒ‹ãƒ³ã‚°ç³»é–‹ç™º â€“â€¯åˆ†æ•£FSç‰¹ã«GlusterFSã®èª¿æŸ»ï¼Œç ”ç©¶ï¼Œãã‚Œã‚’åˆ©ç”¨ã—ãŸé–‹ç™ºåŠã³OSSæ´»å‹• â€“â€¯å®‡å®™èˆªç©ºé–¢é€£å®Ÿè¨¼å®Ÿé¨“åŠã³é–‹ç™º â€“â€¯ãã®ä»–å…ˆç«¯æŠ€è¡“èª¿æŸ» â€¢â€¯ å‡ºå…¥ã‚Šã—ã¦ã„ã‚‹å‹‰å¼·ä¼š â€“â€¯ VIOPS â€¢â€¯ ã“ã®ç•Œéšˆã§ç‰¹ã«é–¢ä¿‚ã®æ·±ã„äºº â€“â€¯ ã‚¯ãƒ©ã‚¦ãƒ‰ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ç ”ç©¶ä¼š â€“â€¯ ä¼Šè—¤æ° ( @tha
yamaz 2011/12/08
hpc
ãƒªãƒ³ã‚¯
How Did AppNexus Scale? CTO Mike Nolet Will Tell You | AppNexus Tech Blog
How Did AppNexus Scale? CTO Mike Nolet Will Tell You AppNexus co-founder and CTO Mike Nolet will present a two-part webinar series on scalability and ad serving in October. Heâ€™ll lead interactive discussions on how AppNexus built a globally distributed infrastructure to manage 14+ billion ads/day with a deep dive into how AppNexus handles this volume, collects and aggregates event data and distrib
yamaz 2011/11/21
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ad

hpc
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RabbitMQ vs Apache ActiveMQ vs Apache qpid
We need a simple message queue to ensure asynchronous message passing across a bunch of our server side apps. The message volume is not intended to be very high, latency is not an issue, and order is not important, but we do need to guarantee that the message will be received and that there is no potential for failure irrespective of infrastructure downtime. Dhruv from my team had taken up the tas
yamaz 2011/01/11
mq

hpc
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How does Google Analytics calculate the number of Unique Visitors for any arbitrary timespan?
yamaz 2011/01/05
analytics

hpc
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ØMQ(zeromq)ã«ã¤ã„ã¦èª¿æŸ»ã™ã‚‹ã€‚
Ã˜MQ(zeromq)ã«ã¤ã„ã¦ç°¡å˜ã«èª¿æŸ»ã—ãŸã®ã§ãƒ¡ãƒ¢ã€‚å…ƒãƒã‚¿ã¯Ã˜MQ - The Guideã€‚ æ¦‚è¦ N-Né€šä¿¡ã‚’å®Ÿç¾ã™ã‚‹ã€socket APIé¢¨è»½é‡ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ãƒ³ã‚°ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã€‚ è‡ªå‹•çš„ãªå†æŽ¥ç¶šã‚„ã€ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ã®ã‚ãƒ¥ãƒ¼ã‚¤ãƒ³ã‚°ã‚’è¡Œã£ã¦ãã‚Œã‚‹ã€‚ è¤‡æ•°ã®ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ãƒ³ã‚°ãƒ‘ã‚¿ãƒ¼ãƒ³ã¨å‘¼ã°ã‚Œã‚‹ã‚‚ã®ã‚’çµ„ã¿åˆã‚ã›ã‚‹ã“ã¨ã«ã‚ˆã£ã¦ã€æŸ”è»Ÿãªãƒ¡ãƒƒã‚»ãƒ¼ã‚¸é…ä¿¡ã‚’è¡Œã†ã“ã¨ãŒã§ãã‚‹ã€‚ ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã«ã¤ã„ã¦ socket APIãƒ©ã‚¤ã‚¯ãªC APIã‚’æŒã¤ã€‚ä»¥ä¸‹socketã¯ã€zeromqã®socketã‚’æŒ‡ã™ã€‚ zeromqã¯ã‚³ãƒ³ãƒ†ã‚ã‚¹ãƒˆã¨ã„ã†ã‚‚ã®ã‚’é€šã˜ã¦ä½¿ã†ã€‚1ã‚³ãƒ³ãƒ†ã‚ã‚¹ãƒˆã«ã€I/Oã‚¹ãƒ¬ãƒƒãƒ‰ãŒ1ã¤å‰²ã‚Šå½“ã¦ã‚‰ã‚Œã‚‹ã€‚åŸºæœ¬1ãƒ—ãƒã‚»ã‚¹ã«1ã‚³ãƒ³ãƒ†ã‚ã‚¹ãƒˆã§OKã€‚è¤‡æ•°ã®contextã‚’æŒã¤ã“ã¨ã¯ã§ãã‚‹ã—ã€ãã®å ´åˆã¯åŒã˜å€‹æ•°ã®I/Oã‚¹ãƒ¬ãƒƒãƒ‰ãŒèµ°ã‚‹ã€‚ zeromqã®socketã¯ã€ãƒ—ãƒã‚»ã‚¹å†…é€šä¿¡(ã‚¹ãƒ¬ãƒƒãƒ‰é–“é€šä¿¡ãªã©)ã€ãƒ—ãƒã‚»ã‚¹é–“é€šä¿¡ã€TCPã€UDPãƒž
yamaz 2010/11/06
mq

hpc
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mixiãŒã¯ã¾ã£ãŸmemcached(or libevent?)ã®å•é¡Œã‚’èª¿ã¹ã‚‹äººãŸã¡
Neal Sato @nealsato äºŒæ—¥ã¨ã‚‚è¤‡æ•°å°ã®memcachedãŒé€£ç¶šã—ã¦è½ã¡ã¾ã—ãŸã€‚ã‚³ã‚¢ã¯åã‹ãšã«ã‚¹ãƒˆãƒ³ã¨è½ã¡ã‚‹ã®ã§ã€åŽŸå› è¿½åŠã«æ™‚é–“ãŒã‹ã‹ã‚Šã¾ã—ãŸãŒã€memcachedã¸ã®æŽ¥ç¶šæ•°ãŒç•°å¸¸ã«å¤šã„ã¨è½ã¡ã‚‹äº‹ã¯å†ç¾ã§ãã¾ã—ãŸã€‚ #mixi 2010-08-12 02:33:00 Neal Sato @nealsato memcachedãŒå¤§é‡ã®æŽ¥ç¶šã‚’å—ã‘ã‚‹ã¨çªç„¶åœæ¢ã‚’ã™ã‚‹ã®ã§ã€memcachedã¸ã®æŽ¥ç¶šæ•°ã‚’æ¸›ã‚‰ã—å®‰å®šé‹ç”¨ä¸ã€‚å¤–éƒ¨ã‹ã‚‰ã®éŽå‰°ã‚¢ã‚¯ã‚»ã‚¹ã§ã¯ãªãã€ã‚µãƒ¼ãƒè¿½åŠ â†’ã‚¯ãƒ©ã‚¤ã‚¢ãƒ³ãƒˆæ•°å¢—åŠ â†’åœæ¢ã€‚ 2010-08-12 08:45:50 Masahiro Nagano / é•·é‡Žé›…åºƒ @kazeburo ãƒ•ã‚¡ã‚¤ãƒ«ãƒ‡ã‚£ã‚¹ã‚¯ãƒªãƒ—ã‚¿ãŒä¸è¶³ã—ã¦memcachedãŒè½ã¡ãŸã¨ã—ã¦ã€ãã®ã¨ãã«ã¯ã€3ä¸‡å¼·ã®æŽ¥ç¶šã¨ãªã£ã¦ã‚‹ã¯ãšã€‚3ä¸‡å¼·ã®æŽ¥ç¶šã¨ãªã‚‹ã«ã¯ã‚¢ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³ã‚µãƒ¼ãƒå´ã®max clientãŒå¹³å‡60ã¨ã—
yamaz 2010/08/13
ã“ã“ã¾ã§èª¿æŸ»ã—ã¦ã‚ã‹ã‚‰ãªã„ã¨ãªã‚‹ã¨ã€ã¡ã‚ƒã‚“ã¨3ä¸‡+ã‚³ãƒã‚¯ã‚·ãƒ§ãƒ³ã‚’ä½œã‚‰ãªã„ã¨å†ç¾ã—ãªã„ã®ã‹ãªã€‚ã‚³ãƒã‚¯ã‚·ãƒ§ãƒ³æ•°32Kã‚ãŸã‚Šã£ã¦ã‚‚å®Ÿã«æ€ªã—ã„ã€‚

hpc
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Ehcache
Ehcache is an open source, standards-based cache that boosts performance, offloads your database, and simplifies scalability. It's the most widely-used Java-based cache because it's robust, proven, full-featured, and integrates with other popular libraries and frameworks. Ehcache scales from in-process caching, all the way to mixed in-process/out-of-process deployments with terabyte-sized caches.
yamaz 2010/02/21
hpc
ãƒªãƒ³ã‚¯
Comparing Memcached and Ehcache Performance | Greg Luck's Blog
yamaz 2010/02/21
hpc
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http://www.polimetrix.com/pycon/slides/
yamaz 2009/12/26
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hpc
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2009/12/10 GPUã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ã®ç¾çŠ¶ã¨ã‚¹ãƒ¼ãƒ‘ãƒ¼ã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ã®æœªæ¥
PFIç¤¾å†…ã‚»ãƒŸãƒŠãƒ¼ 2009å¹´12æœˆ10æ—¥ 20:00-21:00(äºˆå®š) GPUã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ã®ç¾çŠ¶ã¨ã‚¹ãƒ¼ãƒ‘ãƒ¼ã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ã®æœªæ¥ ç™ºè¡¨è€…: æ‘ä¸» å´‡è¡Œï¼ˆãƒ—ãƒªãƒ•ã‚¡ãƒ¼ãƒ‰ã‚¤ãƒ³ãƒ•ãƒ©ã‚¹ãƒˆãƒ©ã‚¯ãƒãƒ£ãƒ¼ ç ”ç©¶é–‹ç™ºéƒ¨é–€ãƒ»äº¬éƒ½å¤§å¦å¤§å¦é™¢ã€€ç‰©ç†å¦ç¬¬äºŒæ•™å®¤ï¼‰ ã‚»ãƒŸãƒŠãƒ¼éŒ²ç”»URL: http://www.ustream.tv/recorded/2837689 ã“ã®ã‚¹ãƒ©ã‚¤ãƒ‰ã¯ã€ç™ºè¡¨å¾Œã«ã¿ãªã•ã¾ã‹ã‚‰ã„ãŸã ã„ãŸè²´é‡ãªæ„è¦‹ã‚’ã‚‚ã¨ã«æ”¹è¨‚ã—ãŸç‰ˆã§ã™ã€‚ç™ºè¡¨æ™‚ç‚¹ã§ã®ç‰ˆã¯ã“ã¡ã‚‰: http://www.slideshare.net/pfi/20091210-gpu-2735685Read less
yamaz 2009/12/10
è¶…å‹‰å¼·ã«ãªã‚Šã¾ã—ãŸï¼Ž

hpc
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é«˜æ ¡ç”Ÿã®ãŸã‚ã® NVIDIA CUDA ã‚µãƒžãƒ¼ã‚ãƒ£ãƒ³ãƒ— 2012 8/30 thu. | NVIDIA
CUDA Zone CUDAÂ® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU â€“ which is optimized for single-threaded performance
yamaz 2009/11/30
GPU

HPC

CUDA
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[pdf]CUDA ãƒ†ã‚¯ãƒ‹ã‚«ãƒ«. ãƒˆãƒ¬ãƒ¼ãƒ‹ãƒ³ã‚°. Vol I: CUDA ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°å…¥é–€. Q2 2008. åˆ¶ä½œãŠã‚ˆã³æä¾›: NVIDIA
CUDA Vol I: CUDA Q2 2008 : NVIDIA GPU ..............................................................................1 CUDA ....................................................................10 CUDA ....................................................................................25 ........................................................................................56 G8x ...................
yamaz 2009/11/30
GPU CUDA

gpu

hpc
ãƒªãƒ³ã‚¯
Welcome to the irqbalance website | Home
IRQBALANCE - handholding your interrupts for power and performance irqbalance is a Linux* daemon that distributes interrupts over the processors and cores you have in your computer system. The design goal of irqbalance is to do find a balance between power savings and optimal performance. To a large degree, the work irqbalance does is invisible to you; if irqbalance performs its job right, nobody
yamaz 2009/11/28
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hpc
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[ãƒ‹ãƒ¥ãƒ¼ã‚¹] æ”¹é€ Firefoxã§æ—¥ç±³é–“6.5Gbpsã®Webã‚¢ã‚¯ã‚»ã‚¹ã€‚æ±å¤§ãŒä¸–ç•Œæœ€é€Ÿé”æˆ
æ±äº¬å¤§å¦å¤§å¦é™¢æƒ…å ±ç†å·¥å¦ç³»ç ”ç©¶ç§‘ã®å¹³æœ¨æ•¬æ•™æŽˆã‚‰ã«ã‚ˆã‚‹ç ”ç©¶ãƒãƒ¼ãƒ ã¯27æ—¥ã€ç±³å›½ã§é–‹å‚¬ã•ã‚ŒãŸã‚¹ãƒ¼ãƒ‘ãƒ¼ã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ã‚¿é–¢é€£ã®å›½éš›ä¼šè°ã€ŒSupercomputing 2009ã€ï¼ˆä¸»å‚¬ï¼šIEEEã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ã‚¿ã‚½ã‚µã‚¨ãƒ†ã‚£ï¼‰ã®ã€Œãƒãƒ³ãƒ‰å¹…ãƒãƒ£ãƒ¬ãƒ³ã‚¸ã€ã§å®Ÿæ–½ã—ãŸæ—¥ç±³é–“ã®é•·è·é›¢ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ãƒ‡ãƒ¼ã‚¿è»¢é€å®Ÿé¨“ã§ã€å°åž‹PCã‚’ç”¨ã„ã¦ã€Webã‚’ä»‹ã—ãŸ6.5Gbpsã®ãƒ‡ãƒ¼ã‚¿è»¢é€é€Ÿåº¦ã‚’é”æˆã—ãŸã¨ç™ºè¡¨ã—ãŸã€‚ ã“ã®ãƒ‡ãƒ¼ã‚¿è»¢é€å®Ÿé¨“ã¯ã€æ±äº¬å¤§å¦ã¨ç±³å›½ãƒãƒ¼ãƒˆãƒ©ãƒ³ãƒ‰ã®ã€ŒSupercomputing 2009ï¼ˆSC09ï¼‰ã€ä¼šå ´ã«è¨ç½®ã—ãŸ2å°ã®å°åž‹PCã¨ã€10Gbpsã®å¦è¡“ç”¨ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã‚’ä½¿ã£ã¦å®Ÿæ–½ã—ãŸã€‚å°åž‹PCã®ä½¿ç”¨éƒ¨å“ã¯ã™ã¹ã¦å¸‚è²©å“ã§æ§‹æˆã•ã‚Œã€CPUã¯ã€ŒIntel Core i7 940ã€ã€ãƒžã‚¶ãƒ¼ãƒœãƒ¼ãƒ‰ã¯ã€ŒASUS Rampage II GENEã€ã€ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã‚¤ãƒ³ã‚¿ãƒ¼ãƒ•ã‚§ã‚¤ã‚¹ã¯ã€ŒChelsio S310ã€ã‚’ä½¿ç”¨ã€‚SSDã¨ã—ã¦ä½¿ç”¨ã™ã‚‹6å€‹ã®ã€Œ
yamaz 2009/11/27
UsadaFox

hpc
ãƒªãƒ³ã‚¯
Tech Reports | EECS at UC Berkeley
Tyson Condie and Neil Conway and Peter Alvaro and Joseph M. Hellerstein and Khaled Elmeleegy and Russell Sears EECS Department, University of California, Berkeley Technical Report No. UCB/EECS-2009-136 October 9, 2009 http://www2.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-136.pdf MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault toleranc
yamaz 2009/11/07
hadoop

hpc

mapreduce
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