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Thread-Aware Adaptive Prefetcher on Multicore Systems: Improving the Performance for Multithreaded Workloads

Authors:

Michael C. HuangAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 13, Issue 1

Article No.: 13, Pages 1 - 25

https://doi.org/10.1145/2890505

Published: 28 March 2016 Publication History

Abstract

Most processors employ hardware data prefetching techniques to hide memory access latencies. However, the prefetching requests from different threads on a multicore processor can cause severe interference with prefetching and/or demand requests of others. The data prefetching can lead to significant performance degradation due to shared resource contention on shared memory multicore systems. This article proposes a thread-aware data prefetching mechanism based on low-overhead runtime information to tune prefetching modes and aggressiveness, mitigating the resource contention in the memory system. Our solution has three new components: (1) a self-tuning prefetcher that uses runtime feedback to dynamically adjust data prefetching modes and arguments of each thread, (2) a filtering mechanism that informs the hardware about which prefetching request can cause shared data invalidation and should be discarded, and (3) a limiter thread acceleration mechanism to estimate and accelerate the critical thread which has the longest completion time in the parallel region of execution. On a set of multithreaded parallel benchmarks, our thread-aware data prefetching mechanism improves the overall performance of 64-core system by 13% over a multimode prefetch baseline system with two-level cache organization and conventional modified, exclusive, shared, and invalid-based directory coherence protocol. We compare our approach with the feedback directed prefetching technique and find that it provides 9% performance improvement on multicore systems, while saving the memory bandwidth consumption.

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Cited By

Eris FLouis MEris KAbellán JJoshi A(2022)Puppeteer: A Random Forest Based Manager for Hardware Prefetchers Across the Memory HierarchyACM Transactions on Architecture and Code Optimization10.1145/357030420:1(1-25)Online publication date: 16-Dec-2022
https://dl.acm.org/doi/10.1145/3570304
AlBarakat LGratz PJiménez DAyguadé EHwu WBadia RHofstee H(2020)SB-FetchProceedings of the 34th ACM International Conference on Supercomputing10.1145/3392717.3392735(1-12)Online publication date: 29-Jun-2020
https://dl.acm.org/doi/10.1145/3392717.3392735
Vijaykumar NEbrahimi EHsieh KGibbons PMutlu O(2018)The locality descriptorProceedings of the 45th Annual International Symposium on Computer Architecture10.1109/ISCA.2018.00074(829-842)Online publication date: 2-Jun-2018
https://dl.acm.org/doi/10.1109/ISCA.2018.00074
Show More Cited By

Index Terms

Thread-Aware Adaptive Prefetcher on Multicore Systems: Improving the Performance for Multithreaded Workloads
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
      1. Multiple instruction, multiple data
2. Hardware
  1. Integrated circuits
    1. Semiconductor memory

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Published In

cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 13, Issue 1

April 2016

347 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/2899032

Editor:
Koen De Bosschere
Ghent University

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Copyright © 2016 ACM.

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Publication History

Published: 28 March 2016

Accepted: 01 January 2016

Revised: 01 January 2016

Received: 01 April 2015

Published in TACO Volume 13, Issue 1

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National Science Foundation
National Natural Science Foundation of China
National High Technology Research and Development Program of China
the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing
Huawei Technologies Co., Ltd

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Cited By

Eris FLouis MEris KAbellán JJoshi A(2022)Puppeteer: A Random Forest Based Manager for Hardware Prefetchers Across the Memory HierarchyACM Transactions on Architecture and Code Optimization10.1145/357030420:1(1-25)Online publication date: 16-Dec-2022
https://dl.acm.org/doi/10.1145/3570304
AlBarakat LGratz PJiménez DAyguadé EHwu WBadia RHofstee H(2020)SB-FetchProceedings of the 34th ACM International Conference on Supercomputing10.1145/3392717.3392735(1-12)Online publication date: 29-Jun-2020
https://dl.acm.org/doi/10.1145/3392717.3392735
Vijaykumar NEbrahimi EHsieh KGibbons PMutlu O(2018)The locality descriptorProceedings of the 45th Annual International Symposium on Computer Architecture10.1109/ISCA.2018.00074(829-842)Online publication date: 2-Jun-2018
https://dl.acm.org/doi/10.1109/ISCA.2018.00074
Guo HHuang LLu YMa JQian CMa SWang Z(2018)Accelerating BFS via Data Structure-Aware Prefetching on GPUIEEE Access10.1109/ACCESS.2018.2876201(1-1)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2876201
Xiong DHuang KJiang XYan X(2017)Providing Predictable Performance via a Slowdown Estimation ModelACM Transactions on Architecture and Code Optimization10.1145/312445114:3(1-26)Online publication date: 22-Aug-2017
https://dl.acm.org/doi/10.1145/3124451

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