Browsing by Keywords "SpGEMM"
Now showing items 1-6 of 6
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Exploiting locality in sparse matrix-matrix multiplication on many-core rchitectures
(IEEE Computer Society, 2017)Exploiting spatial and temporal localities is investigated for efficient row-by-row parallelization of general sparse matrix-matrix multiplication (SpGEMM) operation of the form C=A,B on many-core architectures. Hypergraph ... -
Hypergraph models for parallel sparse matrix-matrix multiplication
(Bilkent University, 2015-09)Multiplication of two sparse matrices (i.e., sparse matrix-matrix multiplication, which is abbreviated as SpGEMM) is a widely used kernel in many applications such as molecular dynamics simulations, graph operations, and ... -
Partitioning models for scaling distributed graph computations
(Bilkent University, 2019-09)The focus of this thesis is intelligent partitioning models and methods for scaling the performance of parallel graph computations on distributed-memory systems. Distributed databases utilize graph partitioning to provide ... -
Partitioning models for scaling parallel sparse matrix-matrix multiplication
(Association for Computing Machinery, 2018-04)We investigate outer-product--parallel, inner-product--parallel, and row-by-row-product--parallel formulations of sparse matrix-matrix multiplication (SpGEMM) on distributed memory architectures. For each of these three ... -
Scaling sparse matrix-matrix multiplication in the accumulo database
(Springer, 2019)We propose and implement a sparse matrix-matrix multiplication (SpGEMM) algorithm running on top of Accumulo’s iterator framework which enables high performance distributed parallelism. The proposed algorithm provides ... -
Simultaneous input and output matrix partitioning for outer-product-parallel sparse matrix-matrix multiplication
(Society for Industrial and Applied Mathematics, 2014-10-23)FFor outer-product-parallel sparse matrix-matrix multiplication (SpGEMM) of the form C=A×B, we propose three hypergraph models that achieve simultaneous partitioning of input and output matrices without any replication of ...
