{"title":"建模收集和分散与硬件性能计数器为Xeon Phi","authors":"James Lin, Akira Nukada, S. Matsuoka","doi":"10.1109/CCGrid.2015.59","DOIUrl":null,"url":null,"abstract":"Intel Initial Many-Core Instructions (IMCI) for Xeon Phi introduces hardware-implemented Gather and Scatter (G/S) load/store contents of SIMD registers from/to non-contiguous memory locations. However, they can be one of key performance bottlenecks for Xeon Phi. Modelling G/S can provide insights to the performance on Xeon Phi, however, the existing solution needs a hand-written assembly implementation. Therefore, we modeled G/S with hardware performance counters which can be profiled by the tools like PAPI. We profiled Address Generation Interlock (AGI) events as the number of G/S, estimated the average latency of G/S with VPU_DATA_READ, and combined them to model the total latencies of G/S. We applied our model to the 3D 7-point stencil and the result showed G/S spent nearly 40% of total kernel time. We also validated the model by implementing a G/S- free version with intrinsics. The contribution of the work is a performance model for G/S built with hardware counters. We believe the model can be generally applicable to CPU as well.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"40 1","pages":"713-716"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Modeling Gather and Scatter with Hardware Performance Counters for Xeon Phi\",\"authors\":\"James Lin, Akira Nukada, S. Matsuoka\",\"doi\":\"10.1109/CCGrid.2015.59\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Intel Initial Many-Core Instructions (IMCI) for Xeon Phi introduces hardware-implemented Gather and Scatter (G/S) load/store contents of SIMD registers from/to non-contiguous memory locations. However, they can be one of key performance bottlenecks for Xeon Phi. Modelling G/S can provide insights to the performance on Xeon Phi, however, the existing solution needs a hand-written assembly implementation. Therefore, we modeled G/S with hardware performance counters which can be profiled by the tools like PAPI. We profiled Address Generation Interlock (AGI) events as the number of G/S, estimated the average latency of G/S with VPU_DATA_READ, and combined them to model the total latencies of G/S. We applied our model to the 3D 7-point stencil and the result showed G/S spent nearly 40% of total kernel time. We also validated the model by implementing a G/S- free version with intrinsics. The contribution of the work is a performance model for G/S built with hardware counters. We believe the model can be generally applicable to CPU as well.\",\"PeriodicalId\":6664,\"journal\":{\"name\":\"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing\",\"volume\":\"40 1\",\"pages\":\"713-716\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCGrid.2015.59\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCGrid.2015.59","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling Gather and Scatter with Hardware Performance Counters for Xeon Phi
Intel Initial Many-Core Instructions (IMCI) for Xeon Phi introduces hardware-implemented Gather and Scatter (G/S) load/store contents of SIMD registers from/to non-contiguous memory locations. However, they can be one of key performance bottlenecks for Xeon Phi. Modelling G/S can provide insights to the performance on Xeon Phi, however, the existing solution needs a hand-written assembly implementation. Therefore, we modeled G/S with hardware performance counters which can be profiled by the tools like PAPI. We profiled Address Generation Interlock (AGI) events as the number of G/S, estimated the average latency of G/S with VPU_DATA_READ, and combined them to model the total latencies of G/S. We applied our model to the 3D 7-point stencil and the result showed G/S spent nearly 40% of total kernel time. We also validated the model by implementing a G/S- free version with intrinsics. The contribution of the work is a performance model for G/S built with hardware counters. We believe the model can be generally applicable to CPU as well.