{"title":"垂直窃取:用于3D mpsoc的健壮、位置感知的所有工作负载分配","authors":"A. Marongiu, P. Burgio, L. Benini","doi":"10.1145/1878921.1878952","DOIUrl":null,"url":null,"abstract":"In this paper we address the issue of efficient doall workload distribution on a embedded 3D MPSoC. 3D stacking technology enables low latency and high bandwidth access to multiple, large memory banks in close spatial proximity. In our implementation one silicon layer contains multiple processors, whereas one or more DRAM layers on top host a NUMA memory subsystem. To obtain high locality and balanced workload we consider a two-step approach. First, a compiler pass analyzes memory references in a loop and schedules each iteration to the processor owning the most frequently accessed data. Second, if locality-aware loop parallelization has generated unbalanced workload we allow idle processors to execute part of the remaining work from neighbors by implementing runtime support for work stealing.","PeriodicalId":136293,"journal":{"name":"International Conference on Compilers, Architecture, and Synthesis for Embedded Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Vertical stealing: robust, locality-aware do-all workload distribution for 3D MPSoCs\",\"authors\":\"A. Marongiu, P. Burgio, L. Benini\",\"doi\":\"10.1145/1878921.1878952\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we address the issue of efficient doall workload distribution on a embedded 3D MPSoC. 3D stacking technology enables low latency and high bandwidth access to multiple, large memory banks in close spatial proximity. In our implementation one silicon layer contains multiple processors, whereas one or more DRAM layers on top host a NUMA memory subsystem. To obtain high locality and balanced workload we consider a two-step approach. First, a compiler pass analyzes memory references in a loop and schedules each iteration to the processor owning the most frequently accessed data. Second, if locality-aware loop parallelization has generated unbalanced workload we allow idle processors to execute part of the remaining work from neighbors by implementing runtime support for work stealing.\",\"PeriodicalId\":136293,\"journal\":{\"name\":\"International Conference on Compilers, Architecture, and Synthesis for Embedded Systems\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Compilers, Architecture, and Synthesis for Embedded Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1878921.1878952\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Compilers, Architecture, and Synthesis for Embedded Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1878921.1878952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Vertical stealing: robust, locality-aware do-all workload distribution for 3D MPSoCs
In this paper we address the issue of efficient doall workload distribution on a embedded 3D MPSoC. 3D stacking technology enables low latency and high bandwidth access to multiple, large memory banks in close spatial proximity. In our implementation one silicon layer contains multiple processors, whereas one or more DRAM layers on top host a NUMA memory subsystem. To obtain high locality and balanced workload we consider a two-step approach. First, a compiler pass analyzes memory references in a loop and schedules each iteration to the processor owning the most frequently accessed data. Second, if locality-aware loop parallelization has generated unbalanced workload we allow idle processors to execute part of the remaining work from neighbors by implementing runtime support for work stealing.