sparse matrix
Core Module Optimizing PDE Sparse Matrix Models With HPCG Example
... A fundamentally new computer architecture has been introduced. This architecture is ap- plication compatible with an existing superscalar microprocessor, which can be verified in a systematic, incremental approach ...
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Parallel Multicore CSB Format and Its Sparse Matrix Vector Multiplication
... Sparse Matrix Vector Multiplication (SpMV) is one of the most basic problems in scientific and en- gineering ...compressed sparse row (CSR) format suffer from a number of nonzero elements on each row ...
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Floating point sparse matrix vector multiply for FPGAs
... Large, high density FPGAs with high local distributed memory bandwidth surpass the peak floating- point performance of high-end, general-purpose processors. Microprocessors do not deliver near their peak floating-point ...
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A Note on Quickly Sampling a Sparse Matrix with Low Rank Expectation
... a sparse matrix A with low rank expectation E(A) = X SY T and independent Pois- son ...adjacency matrix A. In sparse graphs, where m = O(n), ignoring log terms, fastRG runs in time ...
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A Novel Processing-In-Memory Architecture for Dense and Sparse Matrix Multiplications
... In order to overcome the growing processing and memory gap, unique and novel archit- ecture solutions are required. Processing-in-Memory (PIM) architectures are designed to alleviate the issue by reducing the total ...
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Streaming reduction circuit for sparse matrix vector multiplication in FPGAs
... Floating point sparse matrix vector multiplications (SM×V) are kernel operations for many scientific algorithms. In these algorithms, the SM×V is often responsible for the biggest part of the processing ...
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Sparse matrix vector multiplication on a field programmable gate array
... the matrix is about 2,5M, this means that the storage of one plan for the complete SMVM requires at least 2,5M * 64 = 160 ...the sparse matrix vector multiplication, the original plan has to be split ...
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Sparse matrix-vector multiplication on network-on-chip
... dense matrix multiplication, the only limiting is the number of the nonzero elements in the sparse ...a sparse matrix or a dense matrix the NoC network does not treat them dif- ...the ...
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Enhancement of digital grayscale image watermarking using sparse matrix
... The previous studies using DWT and IDWT for better imperceptibility and robustness in watermarking highlighted there is a difficulty in determining the best wavelet transform level to obtain best of maximum perceptuality ...
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Accelerating Sparse Matrix Operations in Neural Networks on Graphics Processing Units
... Since matrix operations are used heavily in deep learning, much research has been done on optimizing them on GPUs (Chetlur et ...involving matrix multiplica- tions (Bogoychev et ...using sparse ...
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Sparse Matrix Inversion with Scaled Lasso
... ization on the off-diagonal matrix only. This is refereed to as the graphical Lasso (GLasso) due to the connection of the precision matrix to Gaussian Markov graphical models. In this GLasso framework, ...
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Sparse matrix computations for dynamic network centrality
... communicability matrix was introduced, which converted the time sequence of networks into a single two-dimensional array, with (i, j) element summa- rizing the ability of node i to communicate with node j, using ...
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Linear scaling density functional simulations of charged point defects in Al2O3 using hierarchical sparse matrix algebra
... iteration with ONETEP for these typial systems. As η is varied from η = 0 , at whih point all segments are stored in dense format, to η > 1 , at whih point all segments are stored in sparse format, the ...
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A staggered eigensolver based on sparse matrix bidiagonalization
... For large enough k , the combination of using the GKL-based method to produce a large number of approximate singular vectors, along with the final Rayleigh-Ritz procedure to refine the l[r] ...
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A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty
... covariance matrix estimation, where often the interest is to estimate a sparse matrix, the proposed method is flexible in estimat- ing both a sparse and well-conditioned covariance ...
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Well-organized Data Mining Techniques for Clustering of Users on Web Log Data
... of matrix indicate the visits of the web page in a particular session ...each sparse matrix we can extract the useful knowledge, whether the web pages are accessed by users or ...
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GPU-accelerated surgery simulation for opening a brain fissure
... In neurosurgery, dissection and retraction are basic techniques for approaching the site of pathology. These tech- niques are carefully performed in order to avoid damage to nerve tissues or blood vessels. However, ...
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Scalable Interpretable Multi-Response Regression via SEED
... of sparse reduced-rank regression remains largely ...top-r sparse singu- lar value decomposition from a generalized eigenvalue problem to find the optimal low-rank and sparse matrix ...basic ...
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Consistent metagenomic biomarker detection via robust PCA
... data matrix as a superposition of two matrices. The first matrix is a low-rank matrix that models the abundance levels of the irrelevant ...second matrix is a sparse matrix that ...
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