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Using hardware performance counters to speed up autotuning convergence on GPUs

  • Published:2022-02 Issue: Volume:160 Page:16-35
  • ISSN:0743-7315
  • Container-title:Journal of Parallel and Distributed Computing
  • language:en
  • Short-container-title:Journal of Parallel and Distributed Computing

Author:

Filipovič JiříORCID,Hozzová JanaORCID,Nezarat Amin,Ol'ha JaroslavORCID,Petrovič Filip

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference44 articles.

1. Hardware counters' space reduction for code region characterization;Alcaraz,2019

2. OpenTuner: an extensible framework for program autotuning;Ansel,2014

3. An adaptive performance modeling tool for gpu architectures;Baghsorkhi;SIGPLAN Not.,2010

4. Analyzing cuda workloads using a detailed gpu simulator;Bakhoda,2009

5. Autotuning in high-performance computing applications;Balaprakash;Proc. IEEE,2018

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1. A methodology for comparing optimization algorithms for auto-tuning;Future Generation Computer Systems;2024-10

2. LO-SpMM: Low-cost Search for High-performance SpMM Kernels on GPUs;ACM Transactions on Architecture and Code Optimization;2024-07-29

3. Towards Dynamic Autotuning of SpMV in CUSP Library;2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2024-05-27

4. FAIR Sharing of Data in Autotuning Research (Vision Paper);Companion of the 15th ACM/SPEC International Conference on Performance Engineering;2024-05-07

5. Efficient Code Region Characterization Through Automatic Performance Counters Reduction Using Machine Learning Techniques;Lecture Notes in Computer Science;2024
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