Co-scheduling Amdahl applications on cache-partitioned systems

Author:

Aupy Guillaume1,Benoit Anne2,Dai Sicheng3,Pottier Loïc2,Raghavan Padma4,Robert Yves25,Shantharam Manu6

Affiliation:

1. Inria Centre de recherche Bordeaux Sud-Ouest, Université de Bordeaux, Talence, France

2. Laboratoire LIP, École Normale Supérieure de Lyon, Lyon, France

3. East China Normal University, Shanghai Shi, China

4. Vanderbilt University, Nashville, TN, USA

5. University of Tennessee Knoxville, Knoxville, TN, USA

6. San Diego Supercomputer Center, San Diego, CA, USA

Abstract

Cache-partitioned architectures allow subsections of the shared last-level cache (LLC) to be exclusively reserved for some applications. This technique dramatically limits interactions between applications that are concurrently executing on a multicore machine. Consider n applications that execute concurrently, with the objective to minimize the makespan, defined as the maximum completion time of the n applications. Key scheduling questions are as follows: (i) which proportion of cache and (ii) how many processors should be given to each application? In this article, we provide answers to (i) and (ii) for Amdahl applications. Even though the problem is shown to be NP-complete, we give key elements to determine the subset of applications that should share the LLC (while remaining ones only use their smaller private cache). Building upon these results, we design efficient heuristics for Amdahl applications. Extensive simulations demonstrate the usefulness of co-scheduling when our efficient cache partitioning strategies are deployed.

Publisher

SAGE Publications

Subject

Hardware and Architecture,Theoretical Computer Science,Software

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An adaptive self‐scheduling loop scheduler;Concurrency and Computation: Practice and Experience;2021-12-02

2. Analytical and Numerical Evaluation of Co-Scheduling Strategies and Their Application;Computers;2021-10-02

3. An Analytical Bound for Choosing Trivial Strategies in Co-scheduling;Computational Science and Its Applications – ICCSA 2021;2021

4. Co-scheduling HPC workloads on cache-partitioned CMP platforms;The International Journal of High Performance Computing Applications;2019-05-09

5. Co-Scheduling HPC Workloads on Cache-Partitioned CMP Platforms;2018 IEEE International Conference on Cluster Computing (CLUSTER);2018-09

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