{"title":"基于gpu的时间并行缓存模拟器","authors":"Junjie Ma, Han Wan, Xiaopeng Gao, Xiang Long","doi":"10.1109/YCICT.2010.5713131","DOIUrl":null,"url":null,"abstract":"We present the design of time parallel trace-driven cache simulation for the purpose of evaluating different cache architectures. Due to the long simulation cycles, traditional sequential simulation methods are no longer practical. An obvious way to achieve fast parallel simulation is time parallel. It splits the whole trace into small slices which are assigned to parallel processors for concurrent simulation. In this paper, we introduce a novel time parallel multi-configuration simulation on single pass method. It exploits time partitioning as the main sources of parallelism and takes the full advantage of the computational capability offered by the Compute Unified Device Architecture (CUDA) on the GPU. Our experimental results demonstrate that the cache simulator based on GPU platform gains 1.91× performance improvement compared to traditional serial algorithm.","PeriodicalId":179847,"journal":{"name":"2010 IEEE Youth Conference on Information, Computing and Telecommunications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"GPU-based time parallel cache simulator\",\"authors\":\"Junjie Ma, Han Wan, Xiaopeng Gao, Xiang Long\",\"doi\":\"10.1109/YCICT.2010.5713131\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present the design of time parallel trace-driven cache simulation for the purpose of evaluating different cache architectures. Due to the long simulation cycles, traditional sequential simulation methods are no longer practical. An obvious way to achieve fast parallel simulation is time parallel. It splits the whole trace into small slices which are assigned to parallel processors for concurrent simulation. In this paper, we introduce a novel time parallel multi-configuration simulation on single pass method. It exploits time partitioning as the main sources of parallelism and takes the full advantage of the computational capability offered by the Compute Unified Device Architecture (CUDA) on the GPU. Our experimental results demonstrate that the cache simulator based on GPU platform gains 1.91× performance improvement compared to traditional serial algorithm.\",\"PeriodicalId\":179847,\"journal\":{\"name\":\"2010 IEEE Youth Conference on Information, Computing and Telecommunications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE Youth Conference on Information, Computing and Telecommunications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/YCICT.2010.5713131\",\"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 IEEE Youth Conference on Information, Computing and Telecommunications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/YCICT.2010.5713131","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present the design of time parallel trace-driven cache simulation for the purpose of evaluating different cache architectures. Due to the long simulation cycles, traditional sequential simulation methods are no longer practical. An obvious way to achieve fast parallel simulation is time parallel. It splits the whole trace into small slices which are assigned to parallel processors for concurrent simulation. In this paper, we introduce a novel time parallel multi-configuration simulation on single pass method. It exploits time partitioning as the main sources of parallelism and takes the full advantage of the computational capability offered by the Compute Unified Device Architecture (CUDA) on the GPU. Our experimental results demonstrate that the cache simulator based on GPU platform gains 1.91× performance improvement compared to traditional serial algorithm.