{"title":"RAO-SS：稀疏直接求解器的运行时自动调整工具原型","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":"{\"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}","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}
RAO-SS: A Prototype of Run-time Auto-tuning Facility for Sparse Direct Solvers
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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