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Evaluation of HPC-Big Data Applications Using Cloud Platforms

机译:使用云平台评估HPC大数据应用程序

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The path to HPC-Big Data convergence has resulted in numerous researches that demonstrate the performance trade-off between running applications on supercomputers and cloud platforms. Previous studies typically focus either on scientific HPC benchmarks or previous cloud configurations, failing to consider all the new opportunities offered by current cloud offerings. We present a comparative study of the performance of representative big data benchmarks, or "Big Data Ogres", and HPC benchmarks running on supercomputer and cloud. Our work distinguishes itself from previous studies in a way that we explore the latest generation of compute-optimized Amazon Elastic Compute Cloud instances, C4 for our experimentation on cloud. Our results reveal that Amazon C4 instances with increased compute performance and low variability in results make EC2-based cluster feasible for scientific computing and its applications in simulations, modeling and analysis.
机译:HPC-大数据融合的道路已导致大量研究证明了超级计算机和云平台上正在运行的应用程序之间的性能折衷。先前的研究通常只关注科学的HPC基准测试或先前的云配置,而没有考虑当前云产品所提供的所有新机会。我们对代表性的大数据基准或“大数据食人魔”以及在超级计算机和云上运行的HPC基准的性能进行了比较研究。我们的工作与以往的研究区分开来,我们探索了最新一代的计算优化的Amazon Elastic Compute Cloud实例C4,用于我们在云上进行的实验。我们的结果表明,具有更高的计算性能和较低的结果变异性的Amazon C4实例使基于EC2的集群对于科学计算及其在模拟,建模和分析中的应用而言是可行的。

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