{"title":"X-on-X:异构系统的分布式并行虚拟平台","authors":"Lukas Jünger, Simon Winther, R. Leupers","doi":"10.1109/DSD57027.2022.00028","DOIUrl":null,"url":null,"abstract":"The complexity of modern heterogeneous systems leads to simulation performance problems. We show how heterogeneous system verification can be accelerated using a heterogeneous simulator architecture, by distributing simulations amongst different hosts with a novel SystemC TLM-compliant method. Hosts are combined via a high-speed network to leverage their specific advantages when executing simulation segments. To avoid timing causality problems, a conservative, asynchronous parallel discrete event simulation approach is used. We analyze a machine learning task on an embedded Linux system using an ARMv8 virtual platform containing a commercial deep learning accelerator. There, our approach enables speedups of up to 3.9x.","PeriodicalId":211723,"journal":{"name":"2022 25th Euromicro Conference on Digital System Design (DSD)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"X-on-X: Distributed Parallel Virtual Platforms for Heterogeneous Systems\",\"authors\":\"Lukas Jünger, Simon Winther, R. Leupers\",\"doi\":\"10.1109/DSD57027.2022.00028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The complexity of modern heterogeneous systems leads to simulation performance problems. We show how heterogeneous system verification can be accelerated using a heterogeneous simulator architecture, by distributing simulations amongst different hosts with a novel SystemC TLM-compliant method. Hosts are combined via a high-speed network to leverage their specific advantages when executing simulation segments. To avoid timing causality problems, a conservative, asynchronous parallel discrete event simulation approach is used. We analyze a machine learning task on an embedded Linux system using an ARMv8 virtual platform containing a commercial deep learning accelerator. There, our approach enables speedups of up to 3.9x.\",\"PeriodicalId\":211723,\"journal\":{\"name\":\"2022 25th Euromicro Conference on Digital System Design (DSD)\",\"volume\":\"101 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 25th Euromicro Conference on Digital System Design (DSD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSD57027.2022.00028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 25th Euromicro Conference on Digital System Design (DSD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSD57027.2022.00028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
X-on-X: Distributed Parallel Virtual Platforms for Heterogeneous Systems
The complexity of modern heterogeneous systems leads to simulation performance problems. We show how heterogeneous system verification can be accelerated using a heterogeneous simulator architecture, by distributing simulations amongst different hosts with a novel SystemC TLM-compliant method. Hosts are combined via a high-speed network to leverage their specific advantages when executing simulation segments. To avoid timing causality problems, a conservative, asynchronous parallel discrete event simulation approach is used. We analyze a machine learning task on an embedded Linux system using an ARMv8 virtual platform containing a commercial deep learning accelerator. There, our approach enables speedups of up to 3.9x.