PATSMA: Parameter Auto-tuning for Shared Memory Algorithms

Programs with high levels of complexity often face challenges in adjusting execution parameters, particularly when these parameters vary based on the execution context. These dynamic parameters significantly impact the program's performance, such as loop granularity, which can vary depending on...

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Bibliographic Details
Published in:arXiv.org
Main Authors: Fernandes, Joao B, Felipe H S da Silva, Xavier-de-Souza, Samuel, Assis, Italo A S
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 14.06.2024
Subjects:
Algorithms
Optimization
Parameters
Real time
Simulated annealing
Tuning
ISSN:2331-8422
Online Access:Get full text
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Summary:Programs with high levels of complexity often face challenges in adjusting execution parameters, particularly when these parameters vary based on the execution context. These dynamic parameters significantly impact the program's performance, such as loop granularity, which can vary depending on factors like the execution environment, program input, or the choice of compiler. Given the expensive nature of testing each case individually, one viable solution is to automate parameter adjustments using optimization methods. This article introduces PATSMA, a parameter auto-tuning tool that leverages Coupled Simulated Annealing (CSA) and Nelder-Mead (NM) optimization methods to fine-tune existing parameters in an application. We demonstrate how auto-tuning can contribute to the real-time optimization of parallel algorithms designed for shared memory systems. PATSMA is a C++ library readily available under the MIT license.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
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ISSN:2331-8422
DOI:10.48550/arxiv.2401.07861