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 Xeo...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.展开更多
3D reverse time migration in tiled transversly isotropic(3D RTM-TTI) is the most precise model for complex seismic imaging.However,vast computing time of 3D RTM-TTI prevents it from being widely used,which is addresse...3D reverse time migration in tiled transversly isotropic(3D RTM-TTI) is the most precise model for complex seismic imaging.However,vast computing time of 3D RTM-TTI prevents it from being widely used,which is addressed by providing parallel solutions for 3D RTM-TTI on multicores and many-cores.After data parallelism and memory optimization,the hot spot function of 3D RTMTTI gains 35.99 X speedup on two Intel Xeon CPUs,89.75 X speedup on one Intel Xeon Phi,89.92 X speedup on one NVIDIA K20 GPU compared with serial CPU baseline.This study makes RTM-TTI practical in industry.Since the computation pattern in RTM is stencil,the approaches also benefit a wide range of stencil-based applications.展开更多
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)
文摘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.
基金Supported by the National Natural Science Foundation of China(No.61432018)
文摘3D reverse time migration in tiled transversly isotropic(3D RTM-TTI) is the most precise model for complex seismic imaging.However,vast computing time of 3D RTM-TTI prevents it from being widely used,which is addressed by providing parallel solutions for 3D RTM-TTI on multicores and many-cores.After data parallelism and memory optimization,the hot spot function of 3D RTMTTI gains 35.99 X speedup on two Intel Xeon CPUs,89.75 X speedup on one Intel Xeon Phi,89.92 X speedup on one NVIDIA K20 GPU compared with serial CPU baseline.This study makes RTM-TTI practical in industry.Since the computation pattern in RTM is stencil,the approaches also benefit a wide range of stencil-based applications.
