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Development of benchmark automation suite and evaluation of various high-performance computing systems
Cluster Computing ( IF 4.4 ) Pub Date : 2020-09-10 , DOI: 10.1007/s10586-020-03167-2
Seungwoo Rho , Geunchul Park , Ji Eun Choi , Chan-Yeol Park

This study aimed to develop a dynamic benchmark automation suite to measure a range of benchmark performances and evaluate the various high-performance computing (HPC) systems. Our suite supports an automated scaling test and profiling data based on hardware performance counters to analyze the system characteristics. We selected four HPC benchmarks—STREAM, High-Performance Linpack, High-Performance Conjugate Gradient, and NAS Parallel Benchmark-for experiments and configured testbeds based on five different systems and an Intel Knights Landing (KNL) cluster with 16 nodes. The Intel KNL system showed both unstable memory and high benchmark performances for a specific input range. Modern Intel systems also exhibited proper characteristics on compute-intensive workloads, whereas the up-to-date AMD system showed high efficiency and proper characteristics on memory-intensive and real application workloads. We also verified that each system has an optimal environment and characteristic for various combinations of experimental variables and profiling data.



中文翻译:

开发基准自动化套件并评估各种高性能计算系统

这项研究旨在开发一种动态基准自动化套件,以测量一系列基准性能并评估各种高性能计算(HPC)系统。我们的套件支持基于硬件性能计数器的自动扩展测试和性能分析数据,以分析系统特性。我们选择了四个HPC基准测试-STREAM,高性能Linpack,高性能共轭梯度和NAS并行基准测试,并基于五个不同的系统和具有16个节点的Intel Knights Landing(KNL)集群配置了测试平台。在特定的输入范围内,Intel KNL系统显示出不稳定的内存和较高的基准性能。现代英特尔系统在计算密集型工作负载上也表现出适当的特性,而最新的AMD系统在内存密集型和实际应用程序工作负载方面显示出高效率和适当的特性。我们还验证了每个系统对于实验变量和分析数据的各种组合都具有最佳的环境和特性。

更新日期:2020-09-10
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