{"title":"RAO-SS: A Prototype of Run-time Auto-tuning Facility for Sparse Direct Solvers","authors":"Takahiro Katagiri, Yoshinori Ishii, Hiroki Honda","doi":"arxiv-2408.11880","DOIUrl":null,"url":null,"abstract":"In this paper, a run-time auto-tuning method for performance parameters\naccording to input matrices is proposed. RAO-SS (Run-time Auto-tuning Optimizer\nfor Sparse Solvers), which is a prototype of auto-tuning software using the\nproposed method, is also evaluated. The RAO-SS is implemented with the\nAutopilot, which is middle-ware to support run-time auto-tuning with fuzzy\nlogic function. The target numerical library is the SuperLU, which is a sparse\ndirect solver for linear equations. The result indicated that: (1) the speedup\nfactors of 1.2 for average and 3.6 for maximum to default executions were\nobtained; (2) the software overhead of the Autopilot can be ignored in RAO-SS.","PeriodicalId":501291,"journal":{"name":"arXiv - CS - Performance","volume":"31 3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Performance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.11880","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a run-time auto-tuning method for performance parameters
according to input matrices is proposed. RAO-SS (Run-time Auto-tuning Optimizer
for Sparse Solvers), which is a prototype of auto-tuning software using the
proposed method, is also evaluated. The RAO-SS is implemented with the
Autopilot, which is middle-ware to support run-time auto-tuning with fuzzy
logic function. The target numerical library is the SuperLU, which is a sparse
direct solver for linear equations. The result indicated that: (1) the speedup
factors of 1.2 for average and 3.6 for maximum to default executions were
obtained; (2) the software overhead of the Autopilot can be ignored in RAO-SS.
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