Experiences in autotuning matrix multiplication for energy minimization on GPUs-Reference-Cited by-同舟云学术

Experiences in autotuning matrix multiplication for energy minimization on GPUs

Published:2015-05-20 Issue:17 Volume:27 Page:5096-5113
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat.: Pract. Exper.

Author:

Anzt Hartwig¹,Haugen Blake¹,Kurzak Jakub¹,Luszczek Piotr¹^ORCID,Dongarra Jack¹²³

Affiliation:

1. Department of Electrical Engineering and Computer Science (EECS) University of Tennessee Knoxville TN 37996‐2250 USA

2. Oak Ridge National Laboratory USA

3. University of Manchester UK

Funder

U.S. Department of Energy

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.3516

Reference43 articles.

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3. Unveiling the performance-energy trade-off in iterative linear system solvers for multithreaded processors

4. GenserA BachmannC StegerC WeissR HaidJ.Power emulation based DVFS efficiency investigations for embedded systems. 2010 International Symposium on System on Chip (SOC) Tampere Finland 2010;173–178.

5. VolkovV.Better performance at lower occupancy.GPU Technology Conference Silicon Valley California. Conference Presentation 2015.

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1. Combining multitask and transfer learning with deep Gaussian processes for autotuning-based performance engineering;The International Journal of High Performance Computing Applications;2023-03-30

2. Improving the Performance of Task-Based Linear Algebra Software with Autotuning Techniques on Heterogeneous Architectures;Computational Science – ICCS 2023;2023

3. Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning;2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS);2022-11

4. On the Autotuning of Task-Based Numerical Libraries for Heterogeneous Architectures;Parallel Computing: Technology Trends;2020-03-20

5. Utilizing GPU Performance Counters to Characterize GPU Kernels via Machine Learning;Lecture Notes in Computer Science;2020