Wilfried Kirschenmann, L. Plagne, A. Ponçot, S. Vialle
{"title":"多核cpu和多核gpu上的并行SPN","authors":"Wilfried Kirschenmann, L. Plagne, A. Ponçot, S. Vialle","doi":"10.1080/00411450.2010.533741","DOIUrl":null,"url":null,"abstract":"This paper presents two parallel Simplified PN (SPN) solver implementations for both multi-core Central Processing Units (CPU) and Graphics Processing Units (GPU). For a nuclear operator such as Électricité de France (EDF), the time required to carry out nuclear reactor core simulations is rather critical when dealing with production constraints. The SPN method provides a convenient trade-off between accuracy and numerical complexity and is used in several industrial simulations. The parallelization of the SPN algorithm reduces its computation time. To solve the problem on distributed memory machines such as PC clusters, Domain Decomposition Methods have been investigated. Complementary to this approach, this work aims to use emerging massively parallel processors such as the GPUs as well as current multi-core CPUs. Based on a fine grained parallelism, this solution achieves good performances on desktop machines. Our multi-core CPU and many-core GPU implementations allow us to solve 3D SPN problems, respectively, 10 and 36 times faster than our sequential CPU reference.","PeriodicalId":49420,"journal":{"name":"Transport Theory and Statistical Physics","volume":"39 1","pages":"255 - 281"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/00411450.2010.533741","citationCount":"12","resultStr":"{\"title\":\"Parallel SPN on Multi-Core CPUS and Many-Core GPUS\",\"authors\":\"Wilfried Kirschenmann, L. Plagne, A. Ponçot, S. Vialle\",\"doi\":\"10.1080/00411450.2010.533741\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents two parallel Simplified PN (SPN) solver implementations for both multi-core Central Processing Units (CPU) and Graphics Processing Units (GPU). For a nuclear operator such as Électricité de France (EDF), the time required to carry out nuclear reactor core simulations is rather critical when dealing with production constraints. The SPN method provides a convenient trade-off between accuracy and numerical complexity and is used in several industrial simulations. The parallelization of the SPN algorithm reduces its computation time. To solve the problem on distributed memory machines such as PC clusters, Domain Decomposition Methods have been investigated. Complementary to this approach, this work aims to use emerging massively parallel processors such as the GPUs as well as current multi-core CPUs. Based on a fine grained parallelism, this solution achieves good performances on desktop machines. Our multi-core CPU and many-core GPU implementations allow us to solve 3D SPN problems, respectively, 10 and 36 times faster than our sequential CPU reference.\",\"PeriodicalId\":49420,\"journal\":{\"name\":\"Transport Theory and Statistical Physics\",\"volume\":\"39 1\",\"pages\":\"255 - 281\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/00411450.2010.533741\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transport Theory and Statistical Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/00411450.2010.533741\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transport Theory and Statistical Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/00411450.2010.533741","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
摘要
本文提出了两种适用于多核中央处理器(CPU)和图形处理器(GPU)的并行简化PN (SPN)求解器实现。对于像Électricité de France (EDF)这样的核运营商来说，在处理生产限制时，进行核反应堆堆芯模拟所需的时间是相当关键的。SPN方法在精度和数值复杂性之间提供了一个方便的权衡，并在一些工业模拟中使用。SPN算法的并行化减少了算法的计算时间。为了解决PC集群等分布式存储机器上的问题，研究了领域分解方法。作为这种方法的补充，这项工作旨在使用新兴的大规模并行处理器，如gpu以及当前的多核cpu。基于细粒度并行性，该解决方案在桌面机器上实现了良好的性能。我们的多核CPU和多核GPU实现使我们能够解决3D SPN问题，分别比我们的顺序CPU参考快10倍和36倍。
Parallel SPN on Multi-Core CPUS and Many-Core GPUS
This paper presents two parallel Simplified PN (SPN) solver implementations for both multi-core Central Processing Units (CPU) and Graphics Processing Units (GPU). For a nuclear operator such as Électricité de France (EDF), the time required to carry out nuclear reactor core simulations is rather critical when dealing with production constraints. The SPN method provides a convenient trade-off between accuracy and numerical complexity and is used in several industrial simulations. The parallelization of the SPN algorithm reduces its computation time. To solve the problem on distributed memory machines such as PC clusters, Domain Decomposition Methods have been investigated. Complementary to this approach, this work aims to use emerging massively parallel processors such as the GPUs as well as current multi-core CPUs. Based on a fine grained parallelism, this solution achieves good performances on desktop machines. Our multi-core CPU and many-core GPU implementations allow us to solve 3D SPN problems, respectively, 10 and 36 times faster than our sequential CPU reference.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
