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Distributed TensorFlow Has Arrived - KDNuggets
Distributed TensorFlow Has Arrived Google has open sourced its distributed version of TensorFlow. Get the info on it here, and catch up on some other TensorFlow news at the same time. KDnuggets has taken seriously its role to keep up with the newest releases of major deep learning projects, and in the recent past we have seen landmark such releases from major technology giants and as well as unive
MapReduceã§ãã‚‹10個ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ - データサイエンティスト上ãŒã‚Šã®DXå‚謀・起æ¥å®¶
Hadoopã¨Mahoutã«ã‚ˆã‚Šã€ãƒ“ッグデータã§ã‚‚機械å¦ç¿’ã‚’è¡Œã†ã“ã¨ãŒã§ãã¾ã™ã€‚Mahoutã§å®Ÿè£…ã•ã‚Œã¦ã„る手法ã¯ã€å…¨ã¦åˆ†æ•£å‡¦ç†ã§ãるアルゴリズムã¨ã„ã†ã“ã¨ã«ãªã‚Šã¾ã™ã€‚Mahoutã§å®Ÿè£…ã•ã‚Œã¦ã„るアルゴリズムã¯ã€ã“ã“ã«åˆ—挙ã•ã‚Œã¦ã„ã¾ã™ã€‚è«–æ–‡ã¨ã—ã¦ã‚‚ã€2006å¹´ã«ã€ŒMap-Reduce for Machine Learning on Multicoreã€ã¨ã—ã¦ã„ãã¤ã‹ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ãŒç´¹ä»‹ã•ã‚Œã¦ã„ã¾ã™ã€‚ ãã“ã§ä»Šå›žã¯ã€ï¼ˆä½•ç•ªç…Žã˜ã‹åˆ†ã‹ã‚Šã¾ã›ã‚“ãŒè‡ªåˆ†ã®ç†è§£ã®ãŸã‚ã«ã‚‚）ã“ã®è«–æ–‡ã§ç´¹ä»‹ã•ã‚Œã¦ã„るアルゴリズムã¨ã€ã©ã†ã‚„ã£ã¦åˆ†æ•£å‡¦ç†ã™ã‚‹ã®ã‹ã‚’ç°¡å˜ã«ãƒ¡ãƒ¢ã—ã¦ãŠããŸã„ã¨æ€ã„ã¾ã™ã€‚計算ã™ã‚‹ã¹ã統計é‡ãŒã€summation form（足ã—ç®—ã§è¡¨ç¾ã§ãる形）ã«ãªã£ã¦ã„ã‚‹ã‹ã©ã†ã‹ãŒã€é‡è¦ãªãƒã‚¤ãƒ³ãƒˆã§ã™ã€‚ãªã£ã¦ãªã„å ´åˆã¯ã€â€ã†ã¾ãâ€MapReduceã®å½¢ã«ãƒãƒ©ã™å¿…è¦ãŒã‚ã‚Šã¾ã™ã€‚ ※例ã«ã‚ˆã£ã¦ã€é–“é•ã„ãŒã‚ã£ãŸå ´åˆã¯éšæ™‚
Papers
BSPlib related papers This page contains references to papers concerning the implementation of the Oxford BSP toolset. The papers are grouped as follows: Introductory papers on BSP Papers on the implementation of the toolset Papers on the Oxford BSP toolset profiling tools High-level libraries built upon BSPlib A large collection of BSP related papers from colleagues at Oxford can also be found in
Bulk synchronous parallel - Wikipedia
The bulk synchronous parallel (BSP) abstract computer is a bridging model for designing parallel algorithms. It is similar to the parallel random access machine (PRAM) model, but unlike PRAM, BSP does not take communication and synchronization for granted. In fact, quantifying the requisite synchronization and communication is an important part of analyzing a BSP algorithm. The BSP model was devel
MapReduce以外ã®åˆ†æ•£å‡¦ç†åŸºç›¤BSP, Piccolo, Sparkã®ç´¹ä»‹ - Preferred Networks Research & Development
ã©ã†ã‚‚，実ã¯ä»Šå¹´ã‹ã‚‰é–‹ç™ºãƒãƒ¼ãƒ ã«joinã—ã¦ã„ãŸä¸å·ã§ã™ï¼Žå¯æ„›ã„犬ã®å†™çœŸãŒãªã‹ã£ãŸã®ã§ï¼Œå¯æ„›ã„マスコットã®ç”»åƒã‚’è²¼ã£ã¦ãŠãã¾ã™ï¼Ž 最近MapReduceã¨ã‹ãã®å®Ÿè£…ã§ã‚ã‚‹Hadoopã¨ã‹ã‚’よãèžãよã†ã«ãªã‚Šã¾ã—ãŸï¼Žã“ã‚Œã¯ã¤ã¾ã‚Šï¼Œãã‚Œã ã‘大é‡ã®ãƒ‡ãƒ¼ã‚¿ã‚’ãªã‚“ã¨ã‹å‡¦ç†ã—ãŸã„ã¨ã„ã†è¦æœ›ãŒã‚ã‚‹ã‹ã‚‰ã ã¨æ€ã„ã¾ã™ï¼Žã—ã‹ã—当ãŸã‚Šå‰ã§ã™ãŒï¼ŒMapReduceã¯éŠ€ã®å¼¾ä¸¸ã§ã¯ã‚ã‚Šã¾ã›ã‚“． ã¨ã„ã†ã“ã¨ã§ï¼Œæœ€è¿‘æ°—ã«ãªã£ã¦ã„ã‚‹MapReduceã¨ã¯é•ã£ãŸã‚¢ãƒ—ãƒãƒ¼ãƒã‚’å–ã£ã¦ã„る分散処ç†åŸºç›¤ã«ã¤ã„ã¦ï¼Œç¤¾å†…ã®TechTalkã§è©±ã—ãŸå†…容を簡å˜ã«ã¾ã¨ã‚ã¦ç´¹ä»‹ã—ãŸã„ã¨æ€ã„ã¾ã™ï¼Ž Bulk Sychronous Parallel ã“ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ 自体ã¯1990å¹´ã«èª•ç”Ÿã—ãŸã‚‚ã®ã§ã™ï¼Žé•·ã„ã®ã§BSPã¨æ›¸ãã¾ã™ï¼Žã•ã¦ï¼Œã‚°ãƒ©ãƒ•ã‹ã‚‰æœ€çŸçµŒè·¯ã‚’求ã‚る時，MapReduceã¯ä½¿ãˆã‚‹ã§ã—ょã†ã‹ï¼Ÿã“ã®ã‚ˆã†ãªè«–æ–‡ãŒå‡ºã‚‹ãらã„ã§ã™ã‹ã‚‰å‡ºæ¥ãªã„ã“ã¨ã¯ã‚
Redis Pub/Sub
How to use pub/sub channels in Redis SUBSCRIBE, UNSUBSCRIBE and PUBLISH implement the Publish/Subscribe messaging paradigm where (citing Wikipedia) senders (publishers) are not programmed to send their messages to specific receivers (subscribers). Rather, published messages are characterized into channels, without knowledge of what (if any) subscribers there may be. Subscribers express interest in
クックパッドã¨Hadoop - クックパッド開発者ブãƒã‚°
ã¯ã˜ã‚ã¾ã—ã¦ã€‚今年ã®5月ã«å…¥ç¤¾ã—ãŸå‹é–“@ã•ãŒã™ãƒãƒ¼ãƒ ã§ã™ã€‚ 入社ã—ã¦ã‹ã‚‰ã¯ã€ãªã‹ãªã‹å¤§å¤‰ãªã“ã¨ã‚‚多ã„ã§ã™ãŒã€æœ€è¿‘ã¯ãŠé…’好ããŒé›†ã¾ã£ã¦æœˆæ›œã‹ã‚‰é£²ã¿åˆã† 「å‹é–“会ã€ãªã‚‹ã‚‚ã®ã‚‚発足ã—ã¦ã€ä»•äº‹é¢ã§ã‚‚仕事以外ã®é¢ã§ã‚‚密度ã®é«˜ã„毎日をéŽã”ã—ã¦ã„ã¾ã™ï¼ ã•ã¦ã€åƒ•ã¯ã€Œã•ãŒã™ã€ãƒãƒ¼ãƒ 所属ã¨ã„ã†ã“ã¨ã§ã€æ™®æ®µã¯ãƒ¬ã‚·ãƒ”を「ã•ãŒã™ã€ãƒ¦ãƒ¼ã‚¶ã®æº€è¶³åº¦ã‚’上ã’ã‚‹ãŸã‚ã«ã€ クックパッドã®æ¤œç´¢ã¾ã‚ã‚Šã«ã¤ã„ã¦ã€ã„ã‚ã„ã‚ãªé–‹ç™ºã‚’è¡Œã£ã¦ã„ã¾ã™ã€‚ 一方ã§ã€ãƒ¦ãƒ¼ã‚¶ã®ã€Œã•ãŒã™æ¬²æ±‚ã€ã«ã¤ã„ã¦æ·±ã知るãŸã‚ã«ã€å¤§è¦æ¨¡ãªãƒ‡ãƒ¼ã‚¿è§£æžã‚’è¡Œã„ã€æ¬²æ±‚ã®åˆ†æžã‚’è¡Œã†æ©Ÿä¼šã‚‚増ãˆã¦ãã¾ã—ãŸã€‚ ã¨ã“ã‚ãŒã€ã‚¯ãƒƒã‚¯ãƒ‘ッドã®ãƒã‚°ã¯è†¨å¤§ãªæ•°ãŒã‚ã‚‹ã®ã§ã€ä¸€å£ã®ãƒ‡ãƒ¼ã‚¿è§£æžã¨è¨€ã£ã¦ã‚‚通常ã®ãƒãƒƒãƒå‡¦ç†ã ã¨é–“ã«åˆã‚ãªã„ãŸã‚〠分散処ç†ç’°å¢ƒã®å¿…è¦æ€§ãŒé«˜ã¾ã£ã¦ãã¾ã—ãŸã€‚ ãã“ã§ã€ã¾ãšã¯æ‰‹è»½ã«è©¦ã›ã‚‹åˆ†æ•£å‡¦ç†ã®çŽ‹é“ã¨ã„ã†ã“ã¨ã§ã€æœ€è¿‘ã§ã¯Hadoopを使ã£ãŸãƒ‡ãƒ¼ã‚¿è§£æžç’°å¢ƒã‚’æ•´å‚™ã—ã¦ã„ã¾ã™ã€‚
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