PhiBench 2.0: characterizing data analytics workloads on Intel Knights Landing  

作　　者：Xie Biwei Zhan Jianfeng Wang Lei Zhang Lixin 解壁伟;Zhan Jianfeng;Wang Lei;Zhang Lixin(Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,P.R.China;University of Chinese Academy of Sciences,Beijing 100049,P.R.China)

机构地区：[1]Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,P.R.China [2]University of Chinese Academy of Sciences,Beijing 100049,P.R.China

出　　处：《High Technology Letters》2019年第2期121-128,共8页高技术通讯（英文版）

基　　金：Supported by the National High Technology Research and Development Program of China(No.2015AA015308);the National Key Research and Development Plan of China(No.2016YFB1000600,2016YFB1000601);the Major Program of National Natural Science Foundation of China(No.61432006)

摘　　要：With high computational capacity, e.g. many-core and wide floating point SIMD units, Intel Xeon Phi shows promising prospect to accelerate high-performance computing(HPC) applications. But the application of Intel Xeon Phi on data analytics workloads in data center is still an open question. Phibench 2.0 is built for the latest generation of Intel Xeon Phi(KNL, Knights Landing), based on the prior work PhiBench(also named BigDataBench-Phi), which is designed for the former generation of Intel Xeon Phi(KNC, Knights Corner). Workloads of PhiBench 2.0 are delicately chosen based on BigdataBench 4.0 and PhiBench 1.0. Other than that, these workloads are well optimized on KNL, and run on real-world datasets to evaluate their performance and scalability. Further, the microarchitecture-level characteristics including CPI, cache behavior, vectorization intensity, and branch prediction efficiency are analyzed and the impact of affinity and scheduling policy on performance are investigated. It is believed that the observations would help other researchers working on Intel Xeon Phi and data analytics workloads.With high computational capacity, e.g. many-core and wide floating point SIMD units, Intel Xeon Phi shows promising prospect to accelerate high-performance computing(HPC) applications. But the application of Intel Xeon Phi on data analytics workloads in data center is still an open question. Phibench 2.0 is built for the latest generation of Intel Xeon Phi(KNL, Knights Landing), based on the prior work PhiBench(also named BigDataBench-Phi), which is designed for the former generation of Intel Xeon Phi(KNC, Knights Corner). Workloads of PhiBench 2.0 are delicately chosen based on BigdataBench 4.0 and PhiBench 1.0. Other than that, these workloads are well optimized on KNL, and run on real-world datasets to evaluate their performance and scalability. Further, the microarchitecture-level characteristics including CPI, cache behavior, vectorization intensity, and branch prediction efficiency are analyzed and the impact of affinity and scheduling policy on performance are investigated. It is believed that the observations would help other researchers working on Intel Xeon Phi and data analytics workloads.

关 键 词：Intel Xeon Phi data analytics workloads characterization Knights Landing(KNL) many core x86 processors 

分 类 号：N[自然科学总论]