The Google File System
The Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung Google∗ ABSTRACT We have designed and implemented the Google File System, a scalable distributed file system for large distributed data-intensive applications. It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients. While sharing
Google の MapReduce: steps to phantasien t(2005-11-16)
2005-11-16 近況 情報処理学会誌に "skeletal paralell programming" というのが載っていて, よく読んでいないのだけれど要は並列計算のためのライブラリを 作りましょうという話のようだった. MPI を土台にしているらしい. Google の MapReduce が引用されており, そんなものがあったと思いだす. 読んでなかった. そういえば前回の ACM Queue も並列計算と CMP の特集. どうも並列計算が基本技能として要求される日は思ったより近い気がしてきた. 勉強しておいた方がよさそうだ. 手始めに "MapReduce: Simplified Data Processing on Large Clusters" を読んでみた. MapReduce というのは並列計算のためのライブラリで, Google のインフラ(ファイルシステムやクラ
Interpreting the Data: Parallel Analysis with Sawzall Rob Pike, Sean Dorward, Robert Griesemer, Sean Quinlan Google, Inc. Abstract Very large data sets often have a flat but regular structure and span multiple disks and machines. Examples include teleph
Interpreting the Data: Parallel Analysis with Sawzall Rob Pike, Sean Dorward, Robert Griesemer, Sean Quinlan Google, Inc. Abstract Very large data sets often have a flat but regular structure and span multiple disks and machines. Examples include telephone call records, network logs, and web document reposi- tories. These large data sets are not amenable to study using traditional database techniq
MapReduce
MapReduce: Simplified Data Processing on Large Clusters Jeffrey Dean and Sanjay Ghemawat jeff@google.com, sanjay@google.com Google, Inc. Abstract MapReduce is a programming model and an associ- ated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that
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