research-article

Machine learning-based prefetch optimization for data center applications

Authors:

Hucheng ZhouAuthors Info & Claims

SC '09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis

Article No.: 56, Pages 1 - 10

https://doi.org/10.1145/1654059.1654116

Published: 14 November 2009 Publication History

Abstract

Performance tuning for data centers is essential and complicated. It is important since a data center comprises thousands of machines and thus a single-digit performance improvement can significantly reduce cost and power consumption. Unfortunately, it is extremely difficult as data centers are dynamic environments where applications are frequently released and servers are continually upgraded.

In this paper, we study the effectiveness of different processor prefetch configurations, which can greatly influence the performance of memory system and the overall data center. We observe a wide performance gap when comparing the worst and best configurations, from 1.4% to 75.1%, for 11 important data center applications. We then develop a tuning framework which attempts to predict the optimal configuration based on hardware performance counters. The framework achieves performance within 1% of the best performance of any single configuration for the same set of applications.

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Cited By

Huang LYan LWu T(2024)Hardware Prefetching Tuning Method Based on Program Phase BehaviorJournal of Circuits, Systems and Computers10.1142/S0218126624501585Online publication date: 28-Mar-2024
https://doi.org/10.1142/S0218126624501585
Pandey SSiddhu LPanda P(2023)NeuroCool: Dynamic Thermal Management of 3D DRAM for Deep Neural Networks through Customized PrefetchingACM Transactions on Design Automation of Electronic Systems10.1145/363001229:1(1-35)Online publication date: 18-Dec-2023
https://dl.acm.org/doi/10.1145/3630012
Cheng CHoltz CKahng ALin BMallappa U(2023)DAGSizer: A Directed Graph Convolutional Network Approach to Discrete Gate Sizing of VLSI GraphsACM Transactions on Design Automation of Electronic Systems10.1145/357701928:4(1-31)Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1145/3577019
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Machine learning-based prefetch optimization for data center applications

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cover image ACM Conferences

SC '09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis

November 2009

778 pages

ISBN:9781605587448

DOI:10.1145/1654059

Conference Chair:
Wilfred Pinfold

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 November 2009

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SC '09: International Conference for High Performance Computing, Networking, Storage and Analysis

November 14 - 20, 2009

Oregon, Portland

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SC '09 Paper Acceptance Rate 59 of 261 submissions, 23%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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Cited By

Huang LYan LWu T(2024)Hardware Prefetching Tuning Method Based on Program Phase BehaviorJournal of Circuits, Systems and Computers10.1142/S0218126624501585Online publication date: 28-Mar-2024
https://doi.org/10.1142/S0218126624501585
Pandey SSiddhu LPanda P(2023)NeuroCool: Dynamic Thermal Management of 3D DRAM for Deep Neural Networks through Customized PrefetchingACM Transactions on Design Automation of Electronic Systems10.1145/363001229:1(1-35)Online publication date: 18-Dec-2023
https://dl.acm.org/doi/10.1145/3630012
Cheng CHoltz CKahng ALin BMallappa U(2023)DAGSizer: A Directed Graph Convolutional Network Approach to Discrete Gate Sizing of VLSI GraphsACM Transactions on Design Automation of Electronic Systems10.1145/357701928:4(1-31)Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1145/3577019
Tseng SHan SWierman A(2023)Trading Throughput for Freshness: Freshness-aware Traffic Engineering and In-Network Freshness ControlACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/35769198:1-2(1-26)Online publication date: 7-Mar-2023
https://dl.acm.org/doi/10.1145/3576919
Adiletta MFargo FDiamond MAdiletta JFranza OSteely S(2023)A Reinforcement Learning Approach to Optimize Cache Prefetcher Aggressiveness at Run-Time2023 Tenth International Conference on Software Defined Systems (SDS)10.1109/SDS59856.2023.10329059(95-102)Online publication date: 23-Oct-2023
https://doi.org/10.1109/SDS59856.2023.10329059
Yang YLi RShi QLi XHu GLi XYuan M(2023)SGDP: A Stream-Graph Neural Network Based Data Prefetcher2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191927(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191927
Scravaglieri LPopov MLima Pilla LGuermouche AAumage OSaillard E(2023)Optimizing performance and energy across problem sizes through a search space exploration and machine learningJournal of Parallel and Distributed Computing10.1016/j.jpdc.2023.104720180(104720)Online publication date: Oct-2023
https://doi.org/10.1016/j.jpdc.2023.104720
Qi XYang JZhang YXiao B(2022)BIOS-Based Server Intelligent OptimizationSensors10.3390/s2218673022:18(6730)Online publication date: 6-Sep-2022
https://doi.org/10.3390/s22186730
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
Lurbe MFeliu JPetit SGomez MSahuquillo J(2022)DeepP: Deep Learning Multi-Program Prefetch Configuration for the IBM POWER 8IEEE Transactions on Computers10.1109/TC.2021.313999771:10(2646-2658)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TC.2021.3139997
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