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Interference-aware co-scheduling method based on classification of application characteristics from hardware performance counter using data mining

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Abstract

Computational scientists and engineers who are eager to obtain the best performance of scientific applications need efficient application characterization methods to successfully exploit high-performance hardware resources. However, modern processors are accompanied by high-bandwidth on-chip memory or a large number of cores. Therefore, application characterization research that takes into account the newly introduced hardware features in next-generation high performance computing environments is insufficient and complex. In this paper, we propose a simple and fast method to classify the application characteristics in systems state-of-the-art processors using hardware performance counters. The proposed method utilizes hardware performance counters to monitor hardware events related to system performance. A clustering approach is adopted that requires limited understanding of the correlation between hardware events and application characteristics. The application characterization technique is applied to NAS parallel benchmarks in two systems, including Intel Knights Landing and SkyLake Xeon processors. We demonstrate that the proposed techniques can capture system and application characteristics and provide users with useful insights into application execution.

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Acknowledgements

This work was partly supported by Institute for Information & communications Technology Promotion (IITP) Grant funded by the Korea government (MSIT) (No. R0190-18-2012) and Korea Institute of Science and Technology Information (KISTI) Grant (No. K-19-L02-C06-S01)

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  1. National Institute of Supercomputing and Networking, KISTI, Daejeon, Republic of Korea

    Jieun Choi, Geunchul Park & Dukyun Nam

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  1. Jieun Choi
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  2. Geunchul Park
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  3. Dukyun Nam
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Correspondence to Dukyun Nam.

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Choi, J., Park, G. & Nam, D. Interference-aware co-scheduling method based on classification of application characteristics from hardware performance counter using data mining. Cluster Comput 23, 57–69 (2020). https://doi.org/10.1007/s10586-019-02949-7

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