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Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

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Conquering Big Data with High Performance Computing

Abstract

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-of-the-art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workflows.

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Notes

  1. 1.

    The current version of Apache SparkTM is optimized when using local disk as an intermediate data storage instead of accessing data from a parallel file system in scientific clusters. However, the lack of local disk in scientific clusters did not impact much on performance. This was because the most of the performance analyses in PATHA were compute bound as the most of the data movement was happened in parsing and loading time.

  2. 2.

    The Edison cluster system for PATHA has different configurations with that of the Edison cluster system for the PTF.

  3. 3.

    The linear regression coefficients are 5. 673 × 10−3 for checkpoint 31 and 8. 515 × 10−4 for checkpoint 36.

  4. 4.

    Please note that the points in Fig. 7.7b, c near (0,[−0.05, −0.25]) are not shown in Fig. 7.7a as they are the part of the major cluster near (0,0).

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Acknowledgements

This work was supported by the Office of Advanced Scientific Computing Research, Office of Science, the U.S. Dept. of Energy, under Contract No. DE-AC02-05CH11231. This work used resources of NERSC. The authors would like to thank Douglas Jacobson, Jay Srinivasan, and Richard Gerber at NERSC, Bryce Foster and Alex Copeland at JGI, and Arie Shoshani at LBNL.

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Authors and Affiliations

  1. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Wucherl Yoo, Alex Sim, Peter Nugent & Kesheng Wu

  2. University of California at Berkeley, Berkeley, CA, USA

    Michelle Koo & Peter Nugent

  3. California Institute of Technology, Pasadena, CA, USA

    Yi Cao

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  1. Wucherl Yoo
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Correspondence to Wucherl Yoo .

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  1. Texas Advanced Computing Center, Austin, Texas, USA

    Ritu Arora

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Yoo, W., Koo, M., Cao, Y., Sim, A., Nugent, P., Wu, K. (2016). Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters. In: Arora, R. (eds) Conquering Big Data with High Performance Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-33742-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-33742-5_7

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