Performance predictors for graphics processing units applied to dark-silicon-aware design space exploration

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Standard

Performance predictors for graphics processing units applied to dark-silicon-aware design space exploration. / Sonohata, Rhayssa; Arigoni, Danillo Christi A.; Fernandes, Eraldo Rezende et al.
in: Concurrency and Computation: Practice and Experience, Jahrgang 35, Nr. 17, e6877, 01.08.2023.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Harvard

APA

Vancouver

Sonohata R, Arigoni DCA, Fernandes ER, Ribeiro dos Santos R, Dessandre Duenha L. Performance predictors for graphics processing units applied to dark-silicon-aware design space exploration. Concurrency and Computation: Practice and Experience. 2023 Aug 1;35(17):e6877. Epub 2022 Mär 4. doi: 10.1002/cpe.6877

Bibtex

@article{d26e304306bd4cd2b493275aecbed0fa,
title = "Performance predictors for graphics processing units applied to dark-silicon-aware design space exploration",
abstract = "The limitations on the scalability of computer systems imposed by the dark-silicon effects are so severe that they support the extensive use of heterogeneity such as the GP-GPU for general purpose processing. Performance simulators of GP-GPU heterogeneous systems aim to provide performance accuracy at the cost of execution time. In this work, we handle time-consuming simulations of design space exploration systems based on GPUs. We have developed performance predictors based on machine learning (ML) algorithms and evaluated them in accuracy and throughput (number of predictions per second). We measure model accuracy through the mean absolute percentage error (MAPE) and the model efficiency through a throughput metric (millions of predictions per second). Our experiments revealed that decision trees predictors are the most promising regarding accuracy and efficiency. We applied the best predictors into the MultiExplorer, a dark silicon-aware design space exploration tool that allows designers to explore the architecture and microarchitecture of multicore/manycore system design.",
keywords = "dark-silicon, design space exploration, GPUs, heterogeneous computing, performance predictors, Business informatics",
author = "Rhayssa Sonohata and Arigoni, {Danillo Christi A.} and Fernandes, {Eraldo Rezende} and {Ribeiro dos Santos}, Ricardo and {Dessandre Duenha}, Liana",
note = "Special Issue: WSCAD 2020. PDCAT 2020/PDCAT‐PAAP 2020 This study was financed in part by FUNDECT and Coordena{\c c}{\~a}o de Aperfei{\c c}oamento de Pessoal de N{\'i}vel Superior ‐ Brasil (CAPES) ‐ Finance Code 001. ",
year = "2023",
month = aug,
day = "1",
doi = "10.1002/cpe.6877",
language = "English",
volume = "35",
journal = "Concurrency and Computation: Practice and Experience",
issn = "1532-0626",
publisher = "John Wiley & Sons Ltd.",
number = "17",

}

RIS

TY - JOUR

T1 - Performance predictors for graphics processing units applied to dark-silicon-aware design space exploration

AU - Sonohata, Rhayssa

AU - Arigoni, Danillo Christi A.

AU - Fernandes, Eraldo Rezende

AU - Ribeiro dos Santos, Ricardo

AU - Dessandre Duenha, Liana

N1 - Special Issue: WSCAD 2020. PDCAT 2020/PDCAT‐PAAP 2020 This study was financed in part by FUNDECT and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior ‐ Brasil (CAPES) ‐ Finance Code 001.

PY - 2023/8/1

Y1 - 2023/8/1

N2 - The limitations on the scalability of computer systems imposed by the dark-silicon effects are so severe that they support the extensive use of heterogeneity such as the GP-GPU for general purpose processing. Performance simulators of GP-GPU heterogeneous systems aim to provide performance accuracy at the cost of execution time. In this work, we handle time-consuming simulations of design space exploration systems based on GPUs. We have developed performance predictors based on machine learning (ML) algorithms and evaluated them in accuracy and throughput (number of predictions per second). We measure model accuracy through the mean absolute percentage error (MAPE) and the model efficiency through a throughput metric (millions of predictions per second). Our experiments revealed that decision trees predictors are the most promising regarding accuracy and efficiency. We applied the best predictors into the MultiExplorer, a dark silicon-aware design space exploration tool that allows designers to explore the architecture and microarchitecture of multicore/manycore system design.

AB - The limitations on the scalability of computer systems imposed by the dark-silicon effects are so severe that they support the extensive use of heterogeneity such as the GP-GPU for general purpose processing. Performance simulators of GP-GPU heterogeneous systems aim to provide performance accuracy at the cost of execution time. In this work, we handle time-consuming simulations of design space exploration systems based on GPUs. We have developed performance predictors based on machine learning (ML) algorithms and evaluated them in accuracy and throughput (number of predictions per second). We measure model accuracy through the mean absolute percentage error (MAPE) and the model efficiency through a throughput metric (millions of predictions per second). Our experiments revealed that decision trees predictors are the most promising regarding accuracy and efficiency. We applied the best predictors into the MultiExplorer, a dark silicon-aware design space exploration tool that allows designers to explore the architecture and microarchitecture of multicore/manycore system design.

KW - dark-silicon

KW - design space exploration

KW - GPUs

KW - heterogeneous computing

KW - performance predictors

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85125564653&partnerID=8YFLogxK

U2 - 10.1002/cpe.6877

DO - 10.1002/cpe.6877

M3 - Journal articles

AN - SCOPUS:85125564653

VL - 35

JO - Concurrency and Computation: Practice and Experience

JF - Concurrency and Computation: Practice and Experience

SN - 1532-0626

IS - 17

M1 - e6877

ER -

DOI

Zuletzt angesehen

Publikationen

  1. Fusion of knowledge bases for better navigation of wheeled mobile robotic group with 3D TVS
  2. Simulation of SARS-CoV-2 pandemic in Germany with ordinary differential equations in MATLAB
  3. Validity claims in context
  4. Attention and the Speed of Information Processing
  5. Fermentative utilization of coffee mucilage using Bacillus coagulans and investigation of down-stream processing of fermentation broth for optically pure L(+)-lactic acid production
  6. Non-acceptances in context
  7. Citizen Action in the Time of the Network
  8. Numerical Investigation of the Effect of Rolling on the Localized Stress and Strain Induction for Wire + Arc Additive Manufactured Structures
  9. Mapping the Order of New Migration
  10. The pace of range expansion
  11. Whose Change is it, Anyway?
  12. On the Existence of Digital Objects
  13. New product development and flawed cause-and-effect relations in strategy maps
  14. Study of non-linear systems
  15. Existenzgründungen junger Handwerksmeister
  16. Sprache und Sprachgebrauch untersuchen in der Primarstufe
  17. Is fairness intuitive? An experiment accounting for subjective utility differences under time pressure
  18. Tristan Garcia, Form and Object
  19. Effect of a Web-Based Guided Self-help Intervention for Prevention of Major Depression in Adults With Subthreshold Depression A Randomized Clinical Trial
  20. Feature selection for density level-sets
  21. Balanced Scorecard implementations – The case of a city hall
  22. Investigation on the Microstructure and Mechanical Properties of Mg–Gd–Nd Ternary Alloys
  23. Working time flexibility and work-life balance
  24. Utilization of protein-rich residues in biotechnological processes
  25. Fluorometer controlled apparatus designed for long-duration algal-feeding experiments and environmental effect studies with mussels