{"title":"OpenMP中多目标工作共享的位置感知内存关联","authors":"T. Scogland, W. Feng","doi":"10.1145/2628071.2671428","DOIUrl":null,"url":null,"abstract":"Heterogeneity is an ever-growing challenge in computing. The clearest example is the increasing popularity of GPUs, and purpose-designed coprocessors such as Intel Xeon Phi. Even disregarding coprocessors, heterogeneity continues to increase with the rise in CPU core counts, adaptive per-core frequencies, and increasingly hierarchical and complex memory systems. Take a system with four memory nodes, associated with four cores each, and four GPUs, each with a distinct address space and tens to hundreds of cores programmed like a bulk-synchronous parallel cluster. In this case, we are effectively programming clusters of miniature constellations in every node.","PeriodicalId":263670,"journal":{"name":"2014 23rd International Conference on Parallel Architecture and Compilation (PACT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Locality-aware memory association for multi-target worksharing in OpenMP\",\"authors\":\"T. Scogland, W. Feng\",\"doi\":\"10.1145/2628071.2671428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Heterogeneity is an ever-growing challenge in computing. The clearest example is the increasing popularity of GPUs, and purpose-designed coprocessors such as Intel Xeon Phi. Even disregarding coprocessors, heterogeneity continues to increase with the rise in CPU core counts, adaptive per-core frequencies, and increasingly hierarchical and complex memory systems. Take a system with four memory nodes, associated with four cores each, and four GPUs, each with a distinct address space and tens to hundreds of cores programmed like a bulk-synchronous parallel cluster. In this case, we are effectively programming clusters of miniature constellations in every node.\",\"PeriodicalId\":263670,\"journal\":{\"name\":\"2014 23rd International Conference on Parallel Architecture and Compilation (PACT)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 23rd International Conference on Parallel Architecture and Compilation (PACT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2628071.2671428\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 23rd International Conference on Parallel Architecture and Compilation (PACT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2628071.2671428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Locality-aware memory association for multi-target worksharing in OpenMP
Heterogeneity is an ever-growing challenge in computing. The clearest example is the increasing popularity of GPUs, and purpose-designed coprocessors such as Intel Xeon Phi. Even disregarding coprocessors, heterogeneity continues to increase with the rise in CPU core counts, adaptive per-core frequencies, and increasingly hierarchical and complex memory systems. Take a system with four memory nodes, associated with four cores each, and four GPUs, each with a distinct address space and tens to hundreds of cores programmed like a bulk-synchronous parallel cluster. In this case, we are effectively programming clusters of miniature constellations in every node.