research-article

VClinic: A Portable and Efficient Framework for Fine-Grained Value Profilers

Authors:

Depei QianAuthors Info & Claims

ASPLOS 2023: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2

Pages 892 - 904

https://doi.org/10.1145/3575693.3576934

Published: 30 January 2023 Publication History

Abstract

Fine-grained value profilers reveal a promising way to accurately detect value-related software inefficiencies with binary instrumentation. Due to the architecture-dependent implementation details of binary instrumentation, existing value profilers suffer from poor portability as well as high engineering efforts to achieve efficiency across platforms. In this paper, we propose VClinic, a portable and efficient fine-grained value profiling framework for analyzing highly optimized binaries on both X86 and ARM platforms. VClinic exploits operand-centric two-level designs in its implementation to provide the common building blocks required for value profilers. By constructing four representative value profilers with VClinic, we demonstrate that VClinic can ease the development of value profilers with portability and efficiency across platforms. Guided by the value profilers built upon VClinic, we can achieve up to 89.94% and 74.66% speedup for real-world programs on X86 and ARM platforms, respectively.

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Cited By

Cui JZhao QHao YLiu XGrosser TDubach CSteuwer MXue JOttoni GQuintão Pereira F(2024)DrPy: Pinpointing Inefficient Memory Usage in Multi-Layer Python ApplicationsProceedings of the 2024 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO57630.2024.10444862(245-257)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1109/CGO57630.2024.10444862
You XYang HLei KLuan ZQian DMohror KArnold DBadia R(2023)TrivialSpy: Identifying Software Triviality via Fine-grained and Dataflow-based Value ProfilingProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607052(1-13)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607052
Lei KDu SYou XXuan ZKong HYang HShang JXiao ZWu ZLuan ZQian D(2023)Accelerating Big Data Application by Eliminating Redundancy on Hadoop Cluster2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS60453.2023.00114(751-756)Online publication date: 17-Dec-2023
https://doi.org/10.1109/ICPADS60453.2023.00114

Index Terms

VClinic: A Portable and Efficient Framework for Fine-Grained Value Profilers
1. General and reference
  1. Cross-computing tools and techniques
    1. Design
    2. Performance
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques
  2. Software notations and tools
    1. Development frameworks and environments

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cover image ACM Conferences

ASPLOS 2023: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2

January 2023

947 pages

ISBN:9781450399166

DOI:10.1145/3575693

General Chair:
Tor M. Aamodt
University of British Columbia, Canada
,
Program Chairs:
Natalie Enright Jerger
University of Toronto, Canada
,
Michael Swift
University of Wisconsin-Madison, USA

Copyright © 2023 ACM.

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Published: 30 January 2023

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Cited By

Cui JZhao QHao YLiu XGrosser TDubach CSteuwer MXue JOttoni GQuintão Pereira F(2024)DrPy: Pinpointing Inefficient Memory Usage in Multi-Layer Python ApplicationsProceedings of the 2024 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO57630.2024.10444862(245-257)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1109/CGO57630.2024.10444862
You XYang HLei KLuan ZQian DMohror KArnold DBadia R(2023)TrivialSpy: Identifying Software Triviality via Fine-grained and Dataflow-based Value ProfilingProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607052(1-13)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607052
Lei KDu SYou XXuan ZKong HYang HShang JXiao ZWu ZLuan ZQian D(2023)Accelerating Big Data Application by Eliminating Redundancy on Hadoop Cluster2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS60453.2023.00114(751-756)Online publication date: 17-Dec-2023
https://doi.org/10.1109/ICPADS60453.2023.00114

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