A simple and efficient storage format for SIMD-accelerated SpMV
Sign Up to like & getrecommendations! Published in 2021 at "Cluster Computing"
DOI: 10.1007/s10586-021-03340-1
Abstract: SpMV (Sparse matrix-vector multiplication) is an essential component in scientific computing and has attracted the attention of researchers in related fields at home and abroad. With the continuous expansion of matrix data, the efficient parallel… read more here.
Keywords: format; sparse matrix; storage format; performance ... See more keywords
Coded Sparse Matrix Computation Schemes That Leverage Partial Stragglers
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Information Theory"
DOI: 10.1109/tit.2022.3152827
Abstract: Distributed matrix computations over large clusters can suffer from the problem of slow or failed worker nodes (called stragglers) which can dominate the overall job execution time. Coded computation utilizes concepts from erasure coding to… read more here.
Keywords: computation; sparse matrix; leverage partial; matrix computation ... See more keywords
Adaptive Hashing With Sparse Matrix Factorization
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2019.2954856
Abstract: Hashing offers a desirable and effective solution for efficiently retrieving the nearest neighbors from large-scale data because of its low storage and computation costs. One of the most appealing techniques for hashing learning is matrix… read more here.
Keywords: matrix factorization; factorization; adaptive hashing; sparse matrix ... See more keywords
Iteratively Reweighted Minimax-Concave Penalty Minimization for Accurate Low-rank Plus Sparse Matrix Decomposition.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2021.3122259
Abstract: Low-rank plus sparse matrix decomposition (LSD) is an important problem in computer vision and machine learning. It has been solved using convex relaxations of the matrix rank and l0-pseudo-norm, which are the nuclear norm and… read more here.
Keywords: low rank; matrix decomposition; rank; plus sparse ... See more keywords
Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Parallel and Distributed Systems"
DOI: 10.1109/tpds.2016.2618791
Abstract: Sparse matrix multiplication is traditionally performed in memory and scales to large matrices using the distributed memory of multiple nodes. In contrast, we scale sparse matrix multiplication beyond memory capacity by implementing sparse matrix dense… read more here.
Keywords: sparse matrix; spmm; memory; matrix multiplication ... See more keywords
Spatiotemporal Graph and Hypergraph Partitioning Models for Sparse Matrix-Vector Multiplication on Many-Core Architectures
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Parallel and Distributed Systems"
DOI: 10.1109/tpds.2018.2864729
Abstract: There exist graph/hypergraph partitioning-based row/column reordering methods for encoding either spatial or temporal locality for sparse matrix-vector multiplication (SpMV) operations. Spatial and temporal hypergraph models in these methods are extended to encapsulate both spatial and… read more here.
Keywords: spatial temporal; locality; sparse matrix; hypergraph ... See more keywords
Level-Based Blocking for Sparse Matrices: Sparse Matrix-Power-Vector Multiplication
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Parallel and Distributed Systems"
DOI: 10.1109/tpds.2022.3223512
Abstract: The multiplication of a sparse matrix with a dense vector (SpMV) is a key component in many numerical schemes and its performance is known to be severely limited by main memory access. Several numerical schemes… read more here.
Keywords: matrix; sparse matrix; multiplication; vector ... See more keywords
Ternary Sparse Matrix Representation for Volumetric Mesh Subdivision and Processing on GPUs
Sign Up to like & getrecommendations! Published in 2017 at "Computer Graphics Forum"
DOI: 10.1111/cgf.13245
Abstract: In this paper, we present a novel volumetric mesh representation suited for parallel computing on modern GPU architectures. The data structure is based on a compact, ternary sparse matrix storage of boundary operators. Boundary operators… read more here.
Keywords: volumetric mesh; sparse matrix; ternary sparse; representation ... See more keywords
Automatic Hyperparameter Tuning in Sparse Matrix Factorization
Sign Up to like & getrecommendations! Published in 2023 at "Neural Computation"
DOI: 10.1162/neco_a_01581
Abstract: Abstract We study the problem of hyperparameter tuning in sparse matrix factorization under a Bayesian framework. In prior work, an analytical solution of sparse matrix factorization with Laplace prior was obtained by a variational Bayes… read more here.
Keywords: matrix factorization; sparse matrix; hyperparameter tuning; matrix ... See more keywords
Streamlined solutions to multilevel sparse matrix problems
Sign Up to like & getrecommendations! Published in 2020 at "Anziam Journal"
DOI: 10.21914/anziamj.v62i0.14621
Abstract: We define and solve classes of sparse matrix problems that arise in multilevel modelling and data analysis. The classes are indexed by the number of nested units, with two-level problems corresponding to the common situation,… read more here.
Keywords: two level; streamlined solutions; level; sparse matrix ... See more keywords
Performance Prediction Based on Statistics of Sparse Matrix-Vector Multiplication on GPUs
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Computational Chemistry"
DOI: 10.4236/jcc.2017.56005
Abstract: As one of the most essential and important operations in linear algebra, the performance prediction of sparse matrix-vector multiplication (SpMV) on GPUs has got more and more attention in recent years. In 2012, Guo and… read more here.
Keywords: sparse matrix; performance prediction; gpus; performance ... See more keywords