Hypergraph partitioning is used in many problem domains including VLSI design, linear algebra, Boolean satisfiability, and data mining. Most versions of this problem are NP-complete or NP-hard, so practical hypergraph partitioners generate approximate partitioning solutions for all but the smallest inputs. One way to speed up hypergraph partitioners is to exploit parallelism. However, existing parallel hypergraph partitioners are not deterministic, which is considered unacceptable in domains like VLSI design where the same partitions must be produced every time a given hypergraph is partitioned.
In this paper, we describe BiPart, the first deterministic, parallel hypergraph partitioner. Experimental results show that BiPart outperforms state-of-the-art hypergraph partitioners in runtime and partition quality while generating partitions deterministically.
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|Understanding and Bridging the Gaps in Current GNN Performance Optimizations|
Kezhao HuangTsinghua University, Jidong ZhaiTsinghua University, Zhen ZhengAlibaba Group, Youngmin YiUniversity of Seoul, Xipeng ShenNorth Carolina State UniversityLink to publication
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Kai WangUniversity of Texas at Austin, Donald FussellUniversity of Texas at Austin, Calvin LinUniversity of Texas at AustinLink to publication
|ShadowVM: Accelerating Data Plane for Data Analytics with Bare Metal CPUs and GPUs|
Zhifang LiEast China Normal University, Mingcong HanEast China Normal University, Shangwei WuEast China Normal University, Chuliang WengEast China Normal UniversityLink to publication
|BiPart: A Parallel and Deterministic Hypergraph Partitioner|
Sepideh MalekiThe University of Texas at Austin, Udit AgarwalUT Austin, Martin BurtscherTexas State University, Keshav PingaliThe University of Texas at AustinLink to publication