research-article

HALWPE: Hardware-Assisted Light Weight Performance Estimation for GPUs

Authors:

Kenneth O'Neal,

Michael KishinevskyAuthors Info & Claims

DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017

Article No.: 80, Pages 1 - 6

https://doi.org/10.1145/3061639.3062257

Published: 18 June 2017 Publication History

Abstract

This paper presents a predictive modeling framework for GPU performance. The key innovation underlying this approach is that performance statistics collected from representative workloads running on current generation GPUs can effectively predict the performance of next-generation GPUs. This is useful when simulators are available for the next-generation device, but simulation times are exorbitant, rendering early design space exploration of microarchitectural parameters and other features infeasible. When predicting performance across three Intel GPU generations (Haswell, Broadwell, Skylake), our models achieved low out-of-sample-errors ranging from 7.45% to 8.91%, while running 30,000-45,000 times faster than cycle-accurate simulation.

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Cited By

Riahi ASavadi ANaghibzadeh M(2024)Many-BSP: an analytical performance model for CUDA kernelsComputing10.1007/s00607-023-01255-w106:5(1519-1555)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s00607-023-01255-w
Carballo-Hernández WPelcat MBhattacharyya SGalán RBerry F(2023)Flydeling: Streamlined Performance Models for Hardware Acceleration of CNNs through System IdentificationACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/35948708:3(1-33)Online publication date: 18-Jul-2023
https://dl.acm.org/doi/10.1145/3594870
Trajkovic JKarimi SHangsan SZhang W(2022)Prediction Modeling for Application-Specific Communication Architecture Design of Optical NoCACM Transactions on Embedded Computing Systems10.1145/352024121:4(1-29)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1145/3520241
Show More Cited By

Index Terms

HALWPE: Hardware-Assisted Light Weight Performance Estimation for GPUs
1. Hardware
  1. Electronic design automation

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cover image ACM Conferences

DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017

June 2017

533 pages

ISBN:9781450349277

DOI:10.1145/3061639

Copyright © 2017 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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EDAC: Electronic Design Automation Consortium
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IEEE-CEDA

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SIGBED: ACM Special Interest Group on Embedded Systems

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New York, NY, United States

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Published: 18 June 2017

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DAC '17: The 54th Annual Design Automation Conference 2017

June 18 - 22, 2017

TX, Austin, USA

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Riahi ASavadi ANaghibzadeh M(2024)Many-BSP: an analytical performance model for CUDA kernelsComputing10.1007/s00607-023-01255-w106:5(1519-1555)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s00607-023-01255-w
Carballo-Hernández WPelcat MBhattacharyya SGalán RBerry F(2023)Flydeling: Streamlined Performance Models for Hardware Acceleration of CNNs through System IdentificationACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/35948708:3(1-33)Online publication date: 18-Jul-2023
https://dl.acm.org/doi/10.1145/3594870
Trajkovic JKarimi SHangsan SZhang W(2022)Prediction Modeling for Application-Specific Communication Architecture Design of Optical NoCACM Transactions on Embedded Computing Systems10.1145/352024121:4(1-29)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1145/3520241
Wu NXie Y(2022)A Survey of Machine Learning for Computer Architecture and SystemsACM Computing Surveys10.1145/349452355:3(1-39)Online publication date: 3-Feb-2022
https://dl.acm.org/doi/10.1145/3494523
Al-Obaidy FMohammadi F(2022)Power-Aware Computing on GPGPU Systems Using ML Classification Techniques2022 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS48785.2022.9937872(1487-1491)Online publication date: 28-May-2022
https://doi.org/10.1109/ISCAS48785.2022.9937872
Liu SLau FSchafer B(2020)Predictive Compositional Method to Design and Reoptimize Complex Behavioral DataflowsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.296644739:10(2615-2627)Online publication date: Oct-2020
https://doi.org/10.1109/TCAD.2020.2966447
Riahi ASavadi ANaghibzadeh M(2020)Comparison of analytical and ML-based models for predicting CPU–GPU data transfer timeComputing10.1007/s00607-019-00780-xOnline publication date: 8-Jan-2020
https://doi.org/10.1007/s00607-019-00780-x
O'Neal KBrisk PShriver EKishinevsky M(2019)Hardware-Assisted Cross-Generation Prediction of GPUs Under DesignIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2018.283439838:6(1133-1146)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1109/TCAD.2018.2834398
O'Neal KBrisk P(2018)Predictive Modeling for CPU, GPU, and FPGA Performance and Power Consumption: A Survey2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)10.1109/ISVLSI.2018.00143(763-768)Online publication date: Jul-2018
https://doi.org/10.1109/ISVLSI.2018.00143

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