{"title":"A GPU Algorithm for IC Floorplanning: Specification, Analysis and Optimization","authors":"Yiding Han, Koushik Chakraborty, Sanghamitra Roy, Vilasita Kuntamukkala","doi":"10.1109/VLSID.2011.19","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel floor planning algorithm for GPUs. Floor planning is an inherently sequential algorithm, far from the typical programs suitable for Single Instruction Multiple Thread (SIMT) style concurrency in a GPU. We propose a fundamentally different approach of exploring the floor plan solution space, where we evaluate concurrent moves on a given floor plan. We illustrate several performance optimization techniques for this algorithm in GPUs. Compared to the sequential algorithm, our techniques achieve 4-30X speedup for a range of MCNC benchmarks, while delivering comparable or better solution quality.","PeriodicalId":371062,"journal":{"name":"2011 24th Internatioal Conference on VLSI Design","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 24th Internatioal Conference on VLSI Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VLSID.2011.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, we propose a novel floor planning algorithm for GPUs. Floor planning is an inherently sequential algorithm, far from the typical programs suitable for Single Instruction Multiple Thread (SIMT) style concurrency in a GPU. We propose a fundamentally different approach of exploring the floor plan solution space, where we evaluate concurrent moves on a given floor plan. We illustrate several performance optimization techniques for this algorithm in GPUs. Compared to the sequential algorithm, our techniques achieve 4-30X speedup for a range of MCNC benchmarks, while delivering comparable or better solution quality.
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