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BOPS, A New Computation-Centric Metric for Datacenter Computing

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Benchmarking, Measuring, and Optimizing (Bench 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12093))

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Abstract

For emerging datacenter (in short, DC) workloads, such as online Internet services or offline data analytics, how to evaluate the upper bound performance and provide apple-to-apple comparisons are fundamental problems. To this end, an unified computation-centric metric is an essential requirement. FLOPS (FLoating-point Operations Per Second) as the most important computation-centric performance metric, has guided computing systems evolutions for many years. However, our observations demonstrate that the average FLOPS efficiency of the DC workloads is only 0.1%, which implies that FLOPS is inappropriate for DC computing. To address the above issue, inspired by FLOPS, we propose BOPS (Basic Operations Per Second), which is the average number of BOPs (Basic OPerations) completed per second, as a new computation-centered metric. We conduct the comprehensive analysis on the characteristics of seventeen typical DC workloads and extract the minimum representative computation operations set, which is composed of integer and floating point computation operations of arithmetic, comparing and array addressing. Then, we propose the formalized BOPS definition and the BOPS based upper bound performance model. Finally, the BOPS measuring tool is also implemented. To validate the BOPS metric, we perform experiments with seventeen DC workloads on three typical Intel processors platforms. First, BOPS can reflect the performance gap of different computing systems, the bias between the peak BOPS performance (obtaining from micro-architecture) gap and the average DC workloads’ wall clock time gap is no more than 10%. Second, BOPS can not only perform the apple-to-apple comparison, but also reflect the upper bound performance of the system. For examples, we analyze the BOPS efficiency of the Redis (the online service) workload and the Sort (the offline analytics) workload. And using the BOPS measuring tool–Sort can achieve 32% BOPS efficiency on the experimental platform.

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Acknowledgment

This work is supported by the National Key Research and Development Plan of China Grant No. 2016YFB1000201.

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Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Lei Wang, Wanling Gao, Kaiyong Yang & Zihan Jiang

Authors
  1. Lei Wang
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  2. Wanling Gao
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  3. Kaiyong Yang
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  4. Zihan Jiang
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Corresponding author

Correspondence to Lei Wang .

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Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Wanling Gao

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Jianfeng Zhan

  3. School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Geoffrey Fox

  4. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

    Xiaoyi Lu

  5. Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX, USA

    Dan Stanzione

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Wang, L., Gao, W., Yang, K., Jiang, Z. (2020). BOPS, A New Computation-Centric Metric for Datacenter Computing. In: Gao, W., Zhan, J., Fox, G., Lu, X., Stanzione, D. (eds) Benchmarking, Measuring, and Optimizing. Bench 2019. Lecture Notes in Computer Science(), vol 12093. Springer, Cham. https://doi.org/10.1007/978-3-030-49556-5_25

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  • DOI: https://doi.org/10.1007/978-3-030-49556-5_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49555-8

  • Online ISBN: 978-3-030-49556-5

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