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A Profiling-Based Approach to Cache Partitioning of Program Data

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Parallel and Distributed Computing, Applications and Technologies (PDCAT 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13798))

Abstract

Cache efficiency is important to avoid unnecessary data transfers and to keep processors active. Cache partitioning, a technique to virtually divide a cache into multiple partitions, has become available in recent hardware. Cache partitioning can improve efficiency by isolating data with high temporal locality to avoid its early eviction before reuse. However, deciding on the partitioning is challenging, because it depends on the locality of reference. To facilitate the decision-making, we propose a profiling-based approach that measures locality, providing knowledge for cache partitioning without requiring manual code analysis. We present a profiling tool and confirm its benefits through experiments on Fujitsu’s A64FX processor, which supports the cache partitioning mechanism called sector cache. Our results show ways to optimize program codes to improve cache efficiency.

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Acknowledgement

This work has received funding from the European High-Performance Joint Undertaking under grant agreement no.956213 (SparCity), and the Federal Ministry of Education and Research of Germany (project number 16HPC045). Performance results have been obtained on systems in the test environment BEAST (Bavarian Energy Architecture & Software Testbed) (https://www.lrz.de/presse/ereignisse/2020-11-06_BEAST/) at the Leibniz Supercomputing Centre.

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Authors and Affiliations

  1. MNM Team, Ludwig-Maximilians-Universität München, Munich, Germany

    Sergej Breiter, Minh Thanh Chung & Karl Fürlinger

  2. Leibniz Supercomputing Centre (LRZ), Garching, Germany

    Josef Weidendorfer

Authors
  1. Sergej Breiter
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  2. Josef Weidendorfer
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  3. Minh Thanh Chung
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  4. Karl Fürlinger
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Corresponding author

Correspondence to Sergej Breiter .

Editor information

Editors and Affiliations

  1. Tohoku University, Aoba-ku, Japan

    Hiroyuki Takizawa

  2. Sun Yat-sen University, Guangzhou, China

    Hong Shen

  3. The University of Tokyo, Tokyo, Japan

    Toshihiro Hanawa

  4. Seoul National University of Science and Technology, Seoul, Korea (Republic of)

    Jong Hyuk Park

  5. Griffith University, Queensland, QLD, Australia

    Hui Tian

  6. Tokyo Denki University, Tokyo, Japan

    Ryusuke Egawa

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Breiter, S., Weidendorfer, J., Chung, M.T., Fürlinger, K. (2023). A Profiling-Based Approach to Cache Partitioning of Program Data. In: Takizawa, H., Shen, H., Hanawa, T., Hyuk Park, J., Tian, H., Egawa, R. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2022. Lecture Notes in Computer Science, vol 13798. Springer, Cham. https://doi.org/10.1007/978-3-031-29927-8_35

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  • DOI: https://doi.org/10.1007/978-3-031-29927-8_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29926-1

  • Online ISBN: 978-3-031-29927-8

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