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ang65 id:ang65

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ang65 2015/06/12
mapreduce

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architecture

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GraphLab: A New Parallel Framework for Machine Learning
GraphLab: A Parallel Framework for Machine LearningDesigning and implementing efficient and provably correct parallel machine learning (ML) algorithms can be very challenging. Existing high-level parallel abstractions like MapReduce are often insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common pat
ang65 2012/01/12
parallel

mapreduce

æ©Ÿæ¢°å¦ç¿’

Algorithms
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ang65 2010/10/01
mapreduce

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ä»Šå¦æœŸã¯æ¯Žé€±è«–æ–‡ç´¹ä»‹ã™ã‚‹ãƒã‚¿ã‚’æŽ¢ã™ã®ã‚‚ç–²ã‚Œã‚‹ã®ã§ã€é©å®œè«–æ–‡ç´¹ä»‹ã‚’å…¥ã‚ŒãªãŒã‚‰ã€ Data-Intensive Text Processing With MapReduce (Synthesis Lectures on Human Language Techno logies) ä½œè€…: Jimmy Lin,Chris Dyerå‡ºç‰ˆç¤¾/ãƒ¡ãƒ¼ã‚«ãƒ¼: Morgan and Claypool Publishersç™ºå£²æ—¥: 2010/08/15ãƒ¡ãƒ‡ã‚£ã‚¢: ãƒšãƒ¼ãƒ‘ãƒ¼ãƒãƒƒã‚¯ ã‚¯ãƒªãƒƒã‚¯: 67å›žã“ã®å•†å“ã‚’å«ã‚€ãƒ–ãƒã‚° (6ä»¶) ã‚’è¦‹ã‚‹ã‚’ã—ã°ã‚‰ãèªã‚‚ã†ã‹ã¨æ€ã£ã¦ã„ã‚‹ã€‚ã¡ãªã¿ã« http://www.umiacs.umd.edu/~jimmylin/book.html ã‹ã‚‰å…¨æ–‡ã® PDF ãŒãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ã§ãã‚‹ã®ã§ã€ãã¡ã‚‰ã‚’ä½¿ç”¨äºˆå®šã€‚å°åˆ·ãƒ»è£½æœ¬ã—ã¦ãƒ‘ãƒ©ãƒ‘ãƒ©ã¨ã‚ãã£ã¦ã„ã‚‹ãŒã€ãƒ†ã‚ã‚¹ãƒˆçš„ã«ã¯å‰²ã¨ã„ã„æœ¬ã ã¨æ€ã†ã€‚ã¿ã‚“ãªãŒã“ãž
ang65 2010/04/21
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mapreduce

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Hadoopã‚’ä½¿ã„ã“ãªã™(1)
ã¾ãšã€ 1 ã®å…¥åŠ›ãƒ•ã‚¡ã‚¤ãƒ«ã‚’åˆ†å‰²ã™ã‚‹æ–¹æ³•ã¯ã€InputFormatã‚¯ãƒ©ã‚¹ã®ã€getSplitsé–¢æ•°ã‚’ä¸Šæ›¸ãã™ã‚‹ã“ã¨ã§ã€ã‚«ã‚¹ã‚¿ãƒžã‚¤ã‚ºã§ãã¾ã™ã€‚ ã¾ãŸã€ 3 ã®InputSplitã‹ã‚‰ã€Keyã¨Valueã‚’æŠ½å‡ºã™ã‚‹å‡¦ç†ã‚‚ã€InputFormatã‚¯ãƒ©ã‚¹ã‚’é€šã˜ã¦ã‚«ã‚¹ã‚¿ãƒžã‚¤ã‚ºã§ãã¾ã™ã€‚ InputFormatã®getRecordReaderé–¢æ•°ã‚’é€šã˜ã¦ã€RecordReaderã‚¯ãƒ©ã‚¹ã‚’ç”Ÿæˆã™ã‚‹ã®ã§ã™ãŒã€ã“ã‚Œã«ä»»æ„ã®RecordReaderã‚¯ãƒ©ã‚¹ã‚’æŒ‡å®šã™ã‚Œã°OKã§ã™ã€‚ 2 ã®Mapå‡¦ç†ã§ã™ãŒã€ãƒ¦ãƒ¼ã‚¶ãŒæŒ‡å®šã—ãŸMapperã‚¯ãƒ©ã‚¹ã®å‡¦ç†ã‚’å®Ÿè¡Œã—ã¾ã™ã€‚ Mapperã‚¯ãƒ©ã‚¹ã¯ã€MapRunnerã‚¯ãƒ©ã‚¹ã‚’é€šã˜ã¦ã€åˆæœŸåŒ–å‡¦ç†ã€mapé–¢æ•°ã‚’ç¹°ã‚Šè¿”ã™éŽç¨‹ã€çµ‚äº†å‡¦ç†ã¨ã„ã£ãŸä¸€é€£ã®æµã‚Œã‚’å®Ÿè¡Œã—ã¾ã™ã€‚ MapRunnerã‚¯ãƒ©ã‚¹ã‚’ã‚«ã‚¹ã‚¿ãƒžã‚¤ã‚ºã™ã‚Œã°ã€ã“ã†ã—ãŸæµã‚Œã‚’åˆ¶å¾¡ã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ 0.20.0ã‹ã‚‰ã®æ–°ã—ã„MapRed
ang65 2010/01/29
hadoop

ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«

java

mapreduce
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Orange: Hadoopã§MapReduceã‚’æ›¸ã
è·å ´ã§Iå›ã‹ã‚‰Hadoopã§(Streamã§ã¯ãªãJavaã‚’ä½¿ã£ã¦)MapReduceã‚’æ›¸ãã®ã«é©ã—ãŸãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ãŒæ¬²ã—ã„ã¨ã„ã†è©±ãŒã‚ã£ãŸã®ã§ã€ä¹…ã—ã¶ã‚Šã«ãƒ–ãƒã‚°ã‚’æ›¸ã„ã¦ã¿ã‚‹ã“ã¨ã«ã—ãŸã€‚ ã¾ãã€Streamã‚’åˆ©ç”¨ã™ã‚Œã°ã‚‚ã£ã¨ç°¡å˜ã«æ›¸ã‘ã‚‹ã¨æ€ã†ã®ã§Javaã‚’åˆ©ç”¨ã™ã‚‹æ„å‘³ã‚‚ã‚ã¾ã‚Šãªã„ã¨æ€ã†ã€‚ åŸºæœ¬çš„ã«ã¯ãƒ—ãƒã‚°ãƒ©ãƒ è‡ªä½“ã«ã‚³ãƒ¡ãƒ³ãƒˆã‚’ã—ã¤ã“ã„ãã‚‰ã„ã«æ›¸ã„ãŸã®ã§ã€ãã¡ã‚‰ã‚’å‚ç…§ã—ã¦ã‚‚ã‚‰ã„ãŸã„ã®ã ã‘ã‚Œã©ã‚‚ã“ã®ãƒ—ãƒã‚°ãƒ©ãƒ ã®ç›®çš„ã‚’ç°¡å˜ã«èª¬æ˜Žã™ã‚‹ã¨ã€cabochaã¨ã„ã†æ—¥æœ¬èªžæ§‹æ–‡è§£æžãƒ—ãƒã‚°ãƒ©ãƒ ã®å‡ºåŠ›çµæžœã‹ã‚‰å˜èªžã®é »åº¦ã‚’è¨ˆç®—ã™ã‚‹ã‚‚ã®ã«ãªã£ã¦ã„ã‚‹ã€‚ cabochaã®å‡ºåŠ›çµæžœã¯ä¸‹ã®ã‚ˆã†ã«ãªã£ã¦ã„ã¦ã€ * 0 1D 0/1 0.00000000 ã‚¦ã‚£ã‚ãƒšãƒ‡ã‚£ã‚¢ ã‚¦ã‚£ã‚ãƒšãƒ‡ã‚£ã‚¢ ã‚¦ã‚£ã‚ãƒšãƒ‡ã‚£ã‚¢ åè©ž-ä¸€èˆ¬ O ã« ãƒ‹ ã« åŠ©è©ž-æ ¼åŠ©è©ž-ä¸€èˆ¬ O * 1 -1O 0/0 0.00000000 ã‚ˆã†ã“ã ãƒ¨ã‚¦ã‚³ã‚½ ã‚ˆã†ã“ã
ang65 2010/01/18
hadoop

mapreduce
ãƒªãƒ³ã‚¯
Hadoop + Luceneã§åˆ†æ•£ã‚¤ãƒ³ãƒ‡ã‚¯ã‚·ãƒ³ã‚° - moratorium
Hadoop + Luceneã§åˆ†æ•£ã‚¤ãƒ³ãƒ‡ã‚¯ã‚·ãƒ³ã‚° 2008-08-27 (Wed) 1:07 Hadoop Hadoop (0.17ç³») + Lucene (2.3ç³») ã§æ¤œç´¢ç”¨ã‚¤ãƒ³ãƒ‡ãƒƒã‚¯ã‚¹ã‚’åˆ†æ•£ã‚¤ãƒ³ãƒ‡ã‚¯ã‚·ãƒ³ã‚°ã™ã‚‹ã‚³ãƒ¼ãƒ‰ã‚’å…¬é–‹ã—ã¦ã¿ã¾ã™ã€‚HDDã«çœ ã‚‰ã›ã¦ã‚‹ã®ã¯ã¡ã‚‡ã£ã¨ã‚‚ã£ãŸã„ãªã„ã€‚ ã„ããªã‚Šã‚½ãƒ¼ã‚¹ã‚³ãƒ¼ãƒ‰ã€‚ package net.kzk9; import java.io.*; import java.util.*; import org.apache.hadoop.conf.*; import org.apache.hadoop.fs.*; import org.apache.hadoop.io.*; import org.apache.hadoop.mapred.*; import org.apache.hadoop.util.*; import org.apache.lucene.i
ang65 2010/01/18
hadoop

index

search

åˆ†æ•£

lucene

mapreduce
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Mars: A MapReduce Framework on Graphics Processors
Mars: A MapReduce Framework on Graphics Processors Introduction Mars is a MapReduce framework on graphic processors (GPUs). We develop this framework aiming at providing a generic framework for developers to implement data- and computation-intensive tasks correctly, efficiently, and easily on the GPU. Mars hides the programming complexity of the GPU behind the simple and familiar MapReduce interfa
ang65 2010/01/15
mapreduce

GPU

cuda

framework
ãƒªãƒ³ã‚¯
Hadoopã®æœ€æ–°å‹•å‘ã‚’ã€ŒHadoop World:NY 2009ã€ã®è³‡æ–™ã‹ã‚‰ï¼ˆå‰ç·¨ï¼‰
Hadoopã¯ã€ã‚°ãƒ¼ã‚°ãƒ«ãŒå¤§è¦æ¨¡åˆ†æ•£ã‚·ã‚¹ãƒ†ãƒ ã®ãŸã‚ã«ç”¨ã„ã¦ã„ã‚‹MapReduceã¨ã„ã†æŠ€è¡“ã‚’ã€ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã¨ã—ã¦å®Ÿç¾ã™ã‚‹ãŸã‚ã«é–‹ç™ºã•ã‚ŒãŸJavaãƒ™ãƒ¼ã‚¹ã®ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã§ã™ã€‚é–‹ç™ºãŒå§‹ã¾ã£ãŸã®ã¯2005å¹´é ƒã§ã€å½“æ™‚Yahoo!ã«æ‰€å±žã—ç¾åœ¨ã¯Clouderaã«æ‰€å±žã™ã‚‹Doug Cuttingæ°ãŒä¸å¿ƒã¨ãªã£ã¦é€²ã‚ã¦ãã¾ã—ãŸã€‚ HadoopãŒå®Ÿç¾ã™ã‚‹MapReduceå‡¦ç†ã¨ã¯ã€ç°¡å˜ã«ã„ãˆã°å¤§é‡ã®ãƒ‡ãƒ¼ã‚¿ã‚’å°ã•ãåˆ†å‰²ã—ã¦å¤šæ•°ã®ãƒŽãƒ¼ãƒ‰ã«å‰²ã‚Šå½“ã¦ï¼ˆMapå‡¦ç†ï¼‰ã€å„ãƒŽãƒ¼ãƒ‰ã§å‡¦ç†ã‚’è¡Œã£ãŸã‚‰ãã‚Œã‚’é›†ç´„ã—ã¦çµæžœã‚’å‡ºã™ï¼ˆReduceå‡¦ç†ï¼‰ã€ã¨ã„ã†åˆ†æ•£å‡¦ç†ã®æ–¹æ³•ã§ã™ã€‚æ•°ãƒ†ãƒ©ãƒã‚¤ãƒˆã«ã‚‚ãŠã‚ˆã¶å¤§å®¹é‡ã®ãƒ‡ãƒ¼ã‚¿ã‚’é«˜é€Ÿã‹ã¤ä½Žã‚³ã‚¹ãƒˆã«åˆ†æ•£å‡¦ç†ã™ã‚‹æ–¹æ³•ã¨ã—ã¦æ³¨ç›®ã‚’é›†ã‚ã¦ã„ã¾ã™ã€‚ ãƒ‹ãƒ¥ãƒ¼ãƒ¨ãƒ¼ã‚¯ã§Hadoop WorldãŒé–‹å‚¬ã•ã‚Œã‚‹ ãã®Hadoopã®ã‚«ãƒ³ãƒ•ã‚¡ãƒ¬ãƒ³ã‚¹ã€ŒHadoop Worldï¼šNY 2009ã€ãŒ10æœˆ2æ—¥ã«ãƒ‹ãƒ¥ãƒ¼ãƒ¨ãƒ¼ã‚¯ã§
ang65 2009/11/07
hadoop

è³‡æ–™

MapReduce
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Rubyã§Hadoopã‚’ãƒ©ãƒƒãƒ—ã€åˆ†æ•£å‡¦ç†ãƒ„ãƒ¼ãƒ«ã‚ãƒƒãƒˆãŒç™»å ´ ï¼ ï¼ IT
2009/05/12 ç±³æ–°èžç¤¾å¤§æ‰‹ã®ãƒ‹ãƒ¥ãƒ¼ãƒ¨ãƒ¼ã‚¯ãƒ»ã‚¿ã‚¤ãƒ ã‚ºã¯5æœˆ11æ—¥ã€Rubyã«ã‚ˆã‚‹å¤§è¦æ¨¡åˆ†æ•£å‡¦ç†ã®ãƒ„ãƒ¼ãƒ«ã‚ãƒƒãƒˆã€ŒMap/Reduce Toolkitã€ï¼ˆMRToolkitï¼‰ã‚’GPLv3ã®ä¸‹ã«ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã§å…¬é–‹ã—ãŸã¨ç™ºè¡¨ã—ãŸã€‚MRToolkitã¯ã€ã™ã§ã«ç¨¼åƒã—ã¦ã„ã‚‹ã‚¯ãƒ©ã‚¹ã‚¿ä¸Šã®Hadoopã¨åˆã‚ã›ã¦ä½¿ã†ã“ã¨ã§Rubyã§å®¹æ˜“ã«Map/Reduceå‡¦ç†ã‚’è¨˜è¿°ã™ã‚‹ã“ã¨ãŒã§ãã‚‹ä¸€ç¨®ã®ãƒ©ãƒƒãƒ‘ãƒ¼ã€‚å‡¦ç†è‡ªä½“ã¯HadoopãŒè¡Œã†ã€‚ã™ã§ã«Hadoopã‚’ä½¿ã£ã¦ã„ã‚‹ãƒ¦ãƒ¼ã‚¶ãƒ¼ã§ã‚ã‚Œã°ã€ä¸å°è¦æ¨¡ã®ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã«å¯¾ã—ã¦ã€ã™ãã«MRToolkitã‚’é©ç”¨å¯èƒ½ã¨ã—ã¦ã„ã‚‹ã€‚ ãƒ‡ãƒ•ã‚©ãƒ«ãƒˆã§æœ‰ç”¨ãªMapã€Reduceã®å‡¦ç†ãƒ¢ã‚¸ãƒ¥ãƒ¼ãƒ«ãŒå«ã¾ã‚Œã¦ã„ã¦ã€æ•°è¡Œã®Rubyã‚¹ã‚¯ãƒªãƒ—ãƒˆã‚’æ›¸ãã ã‘ã§ã€ä¾‹ãˆã°è†¨å¤§ãªApacheã®ãƒã‚°ã‹ã‚‰IPã‚¢ãƒ‰ãƒ¬ã‚¹åˆ¥ã®é–²è¦§å±¥æ´ã‚’ã¾ã¨ã‚ã‚‹ã¨ã„ã£ãŸå‡¦ç†ãŒå¯èƒ½ã¨ã„ã†ã€‚ç‹¬è‡ªã«Mapã‚„Reduceã®å‡¦ç†ã‚’å®šç¾©ã™ã‚‹ã“ã¨ã‚‚
ang65 2009/05/13
hadoop

Ruby

mapreduce
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