Search results for "Sparse matrix"

showing 3 items of 23 documents

CUDA-enabled Sparse Matrix–Vector Multiplication on GPUs using atomic operations

2013

We propose the Sliced Coordinate Format (SCOO) for Sparse Matrix-Vector Multiplication on GPUs.An associated CUDA implementation which takes advantage of atomic operations is presented.We propose partitioning methods to transform a given sparse matrix into SCOO format.An efficient Dual-GPU implementation which overlaps computation and communication is described.Extensive performance comparisons of SCOO compared to other formats on GPUs and CPUs are provided. Existing formats for Sparse Matrix-Vector Multiplication (SpMV) on the GPU are outperforming their corresponding implementations on multi-core CPUs. In this paper, we present a new format called Sliced COO (SCOO) and an efficient CUDA i…

SpeedupComputer Networks and CommunicationsComputer scienceSparse matrix-vector multiplicationParallel computingComputer Graphics and Computer-Aided DesignTheoretical Computer ScienceMatrix (mathematics)CUDAArtificial IntelligenceHardware and ArchitectureBenchmark (computing)MultiplicationGeneral-purpose computing on graphics processing unitsSoftwareSparse matrixParallel Computing
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SparseHC: A Memory-efficient Online Hierarchical Clustering Algorithm

2014

Computing a hierarchical clustering of objects from a pairwise distance matrix is an important algorithmic kernel in computational science. Since the storage of this matrix requires quadratic space with respect to the number of objects, the design of memory-efficient approaches is of high importance to this research area. In this paper, we address this problem by presenting a memory-efficient online hierarchical clustering algorithm called SparseHC. SparseHC scans a sorted and possibly sparse distance matrix chunk-by-chunk. Meanwhile, a dendrogram is built by merging cluster pairs as and when the distance between them is determined to be the smallest among all remaining cluster pairs. The k…

sparse matrixClustering high-dimensional dataTheoretical computer scienceonline algorithmsComputer scienceSingle-linkage clusteringComplete-linkage clusteringNearest-neighbor chain algorithmConsensus clusteringmemory-efficient clusteringCluster analysisk-medians clusteringGeneral Environmental ScienceSparse matrix:Engineering::Computer science and engineering [DRNTU]k-medoidsDendrogramConstrained clusteringHierarchical clusteringDistance matrixCanopy clustering algorithmGeneral Earth and Planetary SciencesFLAME clusteringHierarchical clustering of networkshierarchical clusteringAlgorithmProcedia Computer Science
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Compression and load balancing for efficient sparse matrix-vector product on multicore processors and graphics processing units

2021

We contribute to the optimization of the sparse matrix-vector product by introducing a variant of the coordinate sparse matrix format that balances the workload distribution and compresses both the indexing arrays and the numerical information. Our approach is multi-platform, in the sense that the realizations for (general-purpose) multicore processors as well as graphics accelerators (GPUs) are built upon common principles, but differ in the implementation details, which are adapted to avoid thread divergence in the GPU case or maximize compression element-wise (i.e., for each matrix entry) for multicore architectures. Our evaluation on the two last generations of NVIDIA GPUs as well as In…

workload balancingMulti-core processorComputer Networks and CommunicationsComputer sciencesparse matrix-vector productParallel computingLoad balancing (computing)coordinate sparse matrix formatSparse matrix vectorcompressionExascale computingComputer Science ApplicationsTheoretical Computer ScienceComputational Theory and MathematicsCompression (functional analysis)Product (mathematics)Graphicsgraphics processing units (GPUs)multicoreprocessors (CPUs)Software
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