{"title":"Gossamer:使用多核机器的轻量级方法","authors":"J. Roback, G. Andrews","doi":"10.1109/ICPP.2010.12","DOIUrl":null,"url":null,"abstract":"The key to performance improvements in the multi-core era is for software to utilize the available concurrency. This paper presents a lightweight programming framework called Gossamer that is easy to use, enables the solution of a broad range of parallel programming problems, and produces efficient code. Gossamer contains (1) a set of high-level annotations that one adds to a sequential program to specify concurrency and synchronization, (2) a source-to-source translator that produces an optimized program that uses our threading library, and (3) a run-time system that provides efficient threads and synchronization. Gossamer supports iterative and recursive parallelism, pipelined computations, domain decomposition, and MapReduce computations.","PeriodicalId":180554,"journal":{"name":"2010 39th International Conference on Parallel Processing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Gossamer: A Lightweight Approach to Using Multicore Machines\",\"authors\":\"J. Roback, G. Andrews\",\"doi\":\"10.1109/ICPP.2010.12\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The key to performance improvements in the multi-core era is for software to utilize the available concurrency. This paper presents a lightweight programming framework called Gossamer that is easy to use, enables the solution of a broad range of parallel programming problems, and produces efficient code. Gossamer contains (1) a set of high-level annotations that one adds to a sequential program to specify concurrency and synchronization, (2) a source-to-source translator that produces an optimized program that uses our threading library, and (3) a run-time system that provides efficient threads and synchronization. Gossamer supports iterative and recursive parallelism, pipelined computations, domain decomposition, and MapReduce computations.\",\"PeriodicalId\":180554,\"journal\":{\"name\":\"2010 39th International Conference on Parallel Processing\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 39th International Conference on Parallel Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPP.2010.12\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 39th International Conference on Parallel Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPP.2010.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Gossamer: A Lightweight Approach to Using Multicore Machines
The key to performance improvements in the multi-core era is for software to utilize the available concurrency. This paper presents a lightweight programming framework called Gossamer that is easy to use, enables the solution of a broad range of parallel programming problems, and produces efficient code. Gossamer contains (1) a set of high-level annotations that one adds to a sequential program to specify concurrency and synchronization, (2) a source-to-source translator that produces an optimized program that uses our threading library, and (3) a run-time system that provides efficient threads and synchronization. Gossamer supports iterative and recursive parallelism, pipelined computations, domain decomposition, and MapReduce computations.
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