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Hypart: a hybrid technique for practical memory bandwidth partitioning on commodity servers

Authors:

Seongbeom Park,

Myeonggyun Han,

Woongki BaekAuthors Info & Claims

PACT '18: Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques

Article No.: 5, Pages 1 - 14

https://doi.org/10.1145/3243176.3243211

Published: 01 November 2018 Publication History

Abstract

Memory bandwidth is a highly performance-critical shared resource on modern computer systems. To prevent the contention on memory bandwidth among the collocated workloads, prior works have investigated memory bandwidth partitioning techniques. Despite the extensive prior works, it still remains unexplored to characterize the widely-used memory bandwidth partitioning techniques based on various metrics and investigate a hybrid technique that employs multiple memory bandwidth partitioning techniques to improve the overall efficiency.

To bridge this gap, we first present the in-depth characterization of the three widely-used memory bandwidth partitioning techniques (i.e., thread packing, clock modulation, and Intel's Memory Bandwidth Allocation (MBA)) in terms of dynamic range, granularity, and efficiency. Guided by the characterization results, we propose HyPart, a hybrid technique for practical memory bandwidth partitioning on commodity servers. HyPart composes the three memory bandwidth partitioning techniques in a constructive manner and dynamically performs optimizations based on the application characteristics without requiring any offline profiling. Our experimental results demonstrate the effectiveness of HyPart in that it provides a wider dynamic range and finer-grain control of memory bandwidth and achieves significantly higher efficiency than the conventional memory bandwidth partitioning techniques.

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Index Terms

Hypart: a hybrid technique for practical memory bandwidth partitioning on commodity servers
1. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems

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Published In

cover image ACM Conferences

PACT '18: Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques

November 2018

494 pages

ISBN:9781450359863

DOI:10.1145/3243176

General Chair:
Skevos Evripidou
University of Cyprus, Cyprus
,
Program Chairs:
Per Stenström
Chalmers University of Technology, Sweden
,
Michael O'Boyle
University of Edinburgh, UK

Copyright © 2018 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 the author(s) 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: 01 November 2018

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Korea government (MSIP)

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PACT '18

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SIGARCH

PACT '18: International conference on Parallel Architectures and Compilation Techniques

November 1 - 4, 2018

Limassol, Cyprus

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Overall Acceptance Rate 121 of 471 submissions, 26%

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Cited By

Rubio JBilbao CSaez JPrieto-Matias M(2024)Exploiting Elasticity via OS-Runtime Cooperation to Improve CPU Utilization in Multicore Systems2024 32nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)10.1109/PDP62718.2024.00014(35-43)Online publication date: 20-Mar-2024
https://doi.org/10.1109/PDP62718.2024.00014
Saroliya UArima ELiu DSchulz M(2024)Reinforcement Learning-Driven Co-Scheduling and Diverse Resource Assignments on NUMA Systems2024 IEEE 42nd International Conference on Computer Design (ICCD)10.1109/ICCD63220.2024.00034(170-178)Online publication date: 18-Nov-2024
https://doi.org/10.1109/ICCD63220.2024.00034
Chen RPeng WLi YLiu XWang G(2023)Orchid: An Online Learning Based Resource Partitioning Framework for Job Colocation With Multiple ObjectivesIEEE Transactions on Computers10.1109/TC.2023.330395972:12(3443-3457)Online publication date: Dec-2023
https://doi.org/10.1109/TC.2023.3303959
Saroliya UArima ELiu DSchulz M(2023)Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach2023 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER52292.2023.00023(185-196)Online publication date: 31-Oct-2023
https://doi.org/10.1109/CLUSTER52292.2023.00023
Han MPark EShin YOh DCho YBaek W(2023)COSMOS: Coordinated Management of Cores, Memory, and Compressed Memory Swap for QoS-Aware and Efficient Workload Consolidation for Memory-Intensive ApplicationsIEEE Access10.1109/ACCESS.2023.333668511(133199-133214)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3336685
Navarro-Torres AAlastruey-Benedé JIbáñez PViñals-Yúfera V(2023)BALANCER: bandwidth allocation and cache partitioning for multicore processorsThe Journal of Supercomputing10.1007/s11227-023-05070-079:9(10252-10276)Online publication date: 4-Feb-2023
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Chen RShi HWu JLi YLiu XWang G(2023)GCNPart: Interference-Aware Resource Partitioning Framework with Graph Convolutional Neural Networks and Deep Reinforcement LearningAlgorithms and Architectures for Parallel Processing10.1007/978-3-031-22677-9_30(568-589)Online publication date: 11-Jan-2023
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Wang KLi YWang CJia TChow KWen YDou YXu GHou CYao JZhang L(2022)Characterizing Job Microarchitectural Profiles at Scale: Dataset and AnalysisProceedings of the 51st International Conference on Parallel Processing10.1145/3545008.3545026(1-11)Online publication date: 29-Aug-2022
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Li BPatel TSamsi SGadepally VTiwari DGavrilovska AAltınbüken DBinnig C(2022)MISOProceedings of the 13th Symposium on Cloud Computing10.1145/3542929.3563510(173-189)Online publication date: 7-Nov-2022
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