Using FPGAs to Accelerate HPC and Data Analytics on Intel-Based Systems

被引：0

作者：

Steinke, Thomas ^{[1
]}

Suarez, Estela ^{[2
]}

Boku, Taisuke ^{[3
]}

Kumar, Nalini ^{[4
]}

Martin, David E. ^{[5
]}

机构：

[1] Zuse Inst Berlin ZIB, Takustr 7, D-14195 Berlin, Germany

[2] Forschungszentrum Julich, Julich Supercomp Ctr JSC, Julich, Germany

[3] Univ Tsukuba, Tsukuba, Ibaraki 3058577, Japan

[4] Intel Corp, Santa Clara, CA 95054 USA

[5] Argonne Natl Lab, Argonne, IL 60657 USA

来源：

HIGH PERFORMANCE COMPUTING: ISC HIGH PERFORMANCE 2019 INTERNATIONAL WORKSHOPS | 2020年 / 11887卷

关键词：

FPGA; Reconfigurable computing; High-performance computing; Data analytics; Machine learning; Intel FPGA ecosystem;

D O I：

10.1007/978-3-030-34356-9_42

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

FPGAs can improve performance, energy efficiency and throughput by boosting computation, I/O and communication operations in HPC, data analytics (DA), and machine learning (ML) work-loads and thus complement general-purpose CPUs and GPUs. Recent innovations in hardware and software technologies make FPGAs increasingly attractive for HPC and DA workloads. This first FPGA-focused workshop organized by the IXPUG community gathered experts in the design, programming and usage of reconfigurable systems for HPC and DA workloads to share there experiences with the community.

引用

页码：561 / 566

页数：6

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