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Basic Linear Algebra Subprograms
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21	High-performance matrix-matrix multiplications of very small matrices I. Masliah2 , A. Abdelfattah1 , A. Haidar1 , S. Tomov1 , M. Baboulin2 , J. Falcou2 , and J. Dongarra1,3 1 Add to Reading List Source URL: icl.cs.utk.edu Language: English - Date: 2016-06-07 14:32:34 Computing Computer architecture Computer hardware Parallel computing GPGPU Numerical linear algebra Numerical software Cache Basic Linear Algebra Subprograms CPU cache Multi-core processor Graphics processing unit
22	Matrix Computations and Optimization in Apache Spark ∗ Reza Bosagh Zadeh Xiangrui Meng Add to Reading List Source URL: matroid.com Language: English - Date: 2016-08-06 03:04:23 Numerical linear algebra Apache Spark Matrix Basic Linear Algebra Subprograms Singular value decomposition Lanczos algorithm Rank ARPACK LAPACK Linear algebra Sparse matrix QR decomposition
23	On the Performance Prediction of BLAS-based Tensor Contractions Elmar Peise Diego Fabregat-Traver Add to Reading List Source URL: www.dcs.warwick.ac.uk Language: English - Date: 2014-11-22 16:55:21 Numerical linear algebra Numerical software Tensors Basic Linear Algebra Subprograms Tensor contraction
24	On the Performance Prediction of BLAS-based Tensor Contractions Elmar Peise, Diego Fabregat-Traver, and Paolo Bientinesi AICES, RWTH Aachen {peise,fabregat,pauldj}@aices.rwth-aachen.de Add to Reading List Source URL: www.dcs.warwick.ac.uk Language: English - Date: 2014-11-13 12:51:32 Numerical linear algebra Tensors Numerical software Basic Linear Algebra Subprograms GPGPU Cache Tensor contraction Tensor Matrix CPU cache Kernel Compute kernel
25	Auto Tuning Method for Deciding Block Size Parameters in Dynamically Load-balanced BLAS Yuta SAWA ⋆ Add to Reading List Source URL: iwapt.org Language: English - Date: 2009-08-03 10:38:03 Numerical linear algebra Numerical software Basic Linear Algebra Subprograms LAPACK XTR Matrix multiplication GotoBLAS Matrix Automatically Tuned Linear Algebra Software
26	CS 267 Final Report Reproducible Parallel Matrix-Vector Multiply Peter AhrensMay 11, Add to Reading List Source URL: bebop.cs.berkeley.edu Language: English - Date: 2015-06-15 14:36:21 Numerical linear algebra Numerical software Vectors Binary operations Vector calculus Basic Linear Algebra Subprograms PBLAS Matrix multiplication Matrix Cross product LAPACK Vector space
27	Microsoft PowerPoint - oski-lacsi-2005 Add to Reading List Source URL: www.cs.rice.edu Language: English - Date: 2005-10-11 22:16:51 Numerical linear algebra Sparse matrices Matrices Kernel Matrix Sparse matrix Basic Linear Algebra Subprograms Sparse matrix-vector multiplication
28	Writing a performance-portable matrix multiplication Add to Reading List Source URL: www.des.udc.es Language: English - Date: 2016-01-27 04:16:13 Parallel computing GPGPU Numerical linear algebra Computational science Programming paradigms OpenCL General-purpose computing on graphics processing units Basic Linear Algebra Subprograms Automatic vectorization Compute kernel Kernel Matrix multiplication algorithm
29	ADVANCES IN THE PARALLELIZATION OF MUSIC AND AUDIO APPLICATIONS Eric Battenberg, Adrian Freed, & David Wessel The Center for New Music and Audio Technologies and The Parallel Computing Laboratory University of California Add to Reading List Source URL: www.ericbattenberg.com Language: English - Date: 2016-04-17 01:51:14 Parallel computing OpenMP Non-negative matrix factorization Multi-core processor Manycore processor OpenCL Thread Graphics processing unit CUDA Convolution Basic Linear Algebra Subprograms Supercomputer
30	10th International Society for Music Information Retrieval Conference (ISMIRACCELERATING NON-NEGATIVE MATRIX FACTORIZATION FOR AUDIO SOURCE SEPARATION ON MULTI-CORE AND MANY-CORE ARCHITECTURES Eric Battenberg Add to Reading List Source URL: www.ericbattenberg.com Language: English - Date: 2016-04-17 01:51:14 Concurrent computing Computing Parallel computing Computer programming OpenMP Thread CUDA Basic Linear Algebra Subprograms OpenCL Non-negative matrix factorization Map Speedup

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