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GPS: A Graph Processing System
"... GPS (for Graph Processing System) is a complete opensource system we developed for scalable, faulttolerant, and easytoprogram execution of algorithms on extremely large graphs. GPS is similar to Google’s proprietary Pregel system [MAB+ 11], with some useful additional functionality described in ..."
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GPS (for Graph Processing System) is a complete opensource system we developed for scalable, faulttolerant, and easytoprogram execution of algorithms on extremely large graphs. GPS is similar to Google’s proprietary Pregel system [MAB+ 11], with some useful additional functionality described in the paper. In distributed graph processing systems like GPS and Pregel, graph partitioning is the problem of deciding which vertices of the graph are assigned to which compute nodes. In addition to presenting the GPS system itself, we describe how we have used GPS to study the effects of different graph partitioning schemes. We present our experiments on the performance of GPS under different static partitioning schemes—assigning vertices to workers “intelligently ” before the computation starts—and with GPS’s dynamic repartitioning feature, which reassigns vertices to different compute nodes during the computation by observing their message sending patterns.
Scalable matrix computations on large scalefree graphs using 2d graph partitioning
 in Supercomputing
"... Scalable parallel computing is essential for processing large scalefree (powerlaw) graphs. The distribution of data across processes becomes important on distributedmemory computers with thousands of cores. It has been shown that twodimensional layouts (edge partitioning) can have significant a ..."
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Cited by 6 (1 self)
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Scalable parallel computing is essential for processing large scalefree (powerlaw) graphs. The distribution of data across processes becomes important on distributedmemory computers with thousands of cores. It has been shown that twodimensional layouts (edge partitioning) can have significant advantages over traditional onedimensional layouts. However, simple 2D block distribution does not use the structure of the graph, and more advanced 2D partitioning methods are too expensive for large graphs. We propose a new twodimensional partitioning algorithm that combines graph partitioning with 2D block distribution. The computational cost of the algorithm is essentially the same as 1D graph partitioning. We study the performance of sparse matrixvector multiplication (SpMV) for scalefree graphs from the web and social networks using several different partitioners and both 1D and 2D data layouts. We show that SpMV run time is reduced by exploiting the graph’s structure. Contrary to popular belief, we observe that current graph and hypergraph partitioners often yield relatively good partitions on scalefree graphs. We demonstrate that our new 2D partitioning method consistently outperforms the other methods considered, for both SpMV and an eigensolver, on matrices with up to 1.6 billion nonzeros using up to 16,384 cores. Keywords parallel computing, graph partitioning, scalefree graphs, sparse matrixvector multiplication, twodimensional distribution ∗Sandia is a multiprogram laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of
A RECURSIVE BIPARTITIONING ALGORITHM FOR PERMUTING SPARSE SQUARE MATRICES INTO BLOCK DIAGONAL FORM WITH OVERLAP
, 2013
"... We investigate the problem of symmetrically permuting a square sparse matrix into a block diagonal form with overlap. This permutation problem arises in the parallelization of an explicit formulation of the multiplicative Schwarz preconditioner and a more recent block overlapping banded linear solv ..."
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We investigate the problem of symmetrically permuting a square sparse matrix into a block diagonal form with overlap. This permutation problem arises in the parallelization of an explicit formulation of the multiplicative Schwarz preconditioner and a more recent block overlapping banded linear solver as well as its application to general sparse linear systems. In order to formulate this permutation problem as a graph theoretical problem, we define a constrained version of the multiway graph partitioning by vertex separator (GPVS) problem, which is referred to as the ordered GPVS (oGPVS) problem. However, existing graph partitioning tools are unable to solve the oGPVS problem. So, we also show how the recursive bipartitioning framework can be utilized for solving the oGPVS problem. For this purpose, we propose a lefttoright bipartitioning approach together with a novel vertex fixation scheme so that existing 2way GPVS tools that support fixed vertices can be effectively and efficiently utilized in the recursive bipartitioning framework. Experimental results on a wide range of matrices confirm the validity of the proposed approach. Key words. sparse square matrices, block diagonal form with overlap, graph partitioning by vertex separator, recursive bipartitioning, partitioning with fixed vertices, combinatorial scientific computing