Parallel graph coloring algorithms for distributed GPU environments

Graph coloring is often used in parallelizing scientific computations that run in distributed and multi-GPU environments; it identifies sets of independent data that can be updated in parallel. Many algorithms exist for graph coloring on a single GPU or in distributed memory, but to the best of our...

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Published in:	Parallel computing Vol. 110; no. C; p. 102896
Main Authors:	Bogle, Ian, Slota, George M., Boman, Erik G., Devine, Karen D., Rajamanickam, Sivasankaran
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01.05.2022 Elsevier
Subjects:	Combinatorial scientific computing Distributed algorithms GPU Graph coloring Combinatorial scientific computing Graph coloring Distributed algorithms GPU
ISSN:	0167-8191, 1872-7336
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Abstract	Graph coloring is often used in parallelizing scientific computations that run in distributed and multi-GPU environments; it identifies sets of independent data that can be updated in parallel. Many algorithms exist for graph coloring on a single GPU or in distributed memory, but to the best of our knowledge, hybrid MPI+GPU algorithms have been unexplored until this work. We present several MPI+GPU coloring approaches based on the distributed coloring algorithms of Gebremedhin et al. and the shared-memory algorithms of Deveci et al. The on-node parallel coloring uses implementations in KokkosKernels, which provide parallelization for both multicore CPUs and GPUs. We further extend our approaches to compute distance-2 and partial distance-2 colorings, giving the first known distributed, multi-GPU algorithm for these problems. In addition, we propose a novel heuristic to reduce communication for recoloring in distributed graph coloring. Our experiments show that our approaches operate efficiently on inputs too large to fit on a single GPU and scale up to graphs with 76.7 billion edges running on 128 GPUs. •We present the first multi-GPU graph coloring implementation.•Our framework solves distance-1, distance-2 and partial distance-2 coloring.•We color a mesh with 12.8 billion vertices and 76 billion edges in under 2 seconds.•Our framework generally uses fewer colors and is faster than Zoltan for distance-1.•Our framework is competitive with Zoltan for other coloring variants.
AbstractList	Graph coloring is often used in parallelizing scientific computations that run in distributed and multi-GPU environments; it identifies sets of independent data that can be updated in parallel. Many algorithms exist for graph coloring on a single GPU or in distributed memory, but to the best of our knowledge, hybrid MPI+GPU algorithms have been unexplored until this work. We present several MPI+GPU coloring approaches based on the distributed coloring algorithms of Gebremedhin et al. and the shared-memory algorithms of Deveci et al. The on-node parallel coloring uses implementations in KokkosKernels, which provide parallelization for both multicore CPUs and GPUs. We further extend our approaches to compute distance-2 and partial distance-2 colorings, giving the first known distributed, multi-GPU algorithm for these problems. In addition, we propose a novel heuristic to reduce communication for recoloring in distributed graph coloring. Our experiments show that our approaches operate efficiently on inputs too large to fit on a single GPU and scale up to graphs with 76.7 billion edges running on 128 GPUs. •We present the first multi-GPU graph coloring implementation.•Our framework solves distance-1, distance-2 and partial distance-2 coloring.•We color a mesh with 12.8 billion vertices and 76 billion edges in under 2 seconds.•Our framework generally uses fewer colors and is faster than Zoltan for distance-1.•Our framework is competitive with Zoltan for other coloring variants.
ArticleNumber	102896
Author	Devine, Karen D. Boman, Erik G. Rajamanickam, Sivasankaran Slota, George M. Bogle, Ian
Author_xml	– sequence: 1 givenname: Ian orcidid: 0000-0001-6591-8785 surname: Bogle fullname: Bogle, Ian email: boglei@rpi.edu organization: Department of Computer Science, Rensselaer Polytechnic Institute, United States of America – sequence: 2 givenname: George M. surname: Slota fullname: Slota, George M. email: slotag@rpi.edu organization: Department of Computer Science, Rensselaer Polytechnic Institute, United States of America – sequence: 3 givenname: Erik G. surname: Boman fullname: Boman, Erik G. email: egboman@sandia.gov organization: Scalable Algorithms Department, Sandia National Laboratories, New Mexico, United States of America – sequence: 4 givenname: Karen D. surname: Devine fullname: Devine, Karen D. email: kddevin@sandia.gov organization: Scalable Algorithms Department, Sandia National Laboratories, New Mexico, United States of America – sequence: 5 givenname: Sivasankaran surname: Rajamanickam fullname: Rajamanickam, Sivasankaran email: srajama@sandia.gov organization: Scalable Algorithms Department, Sandia National Laboratories, New Mexico, United States of America
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SubjectTerms	Combinatorial scientific computing Distributed algorithms GPU Graph coloring
Title	Parallel graph coloring algorithms for distributed GPU environments
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