Matrix-Vector Multiplication
Improving Parallel Sparse Matrix-vector Multiplication
... Sparse Matrix-vector Multiplication (SMvM) is a mathematical technique encountered in many programs and computations and is often heavily ...input vector is multiplied with values from a ...
81
Sparse matrix-vector multiplication on network-on-chip
... ing matrix-vector multiplication by using Network-on-Chip (NoC) ...sparse matrix-vector multiplication (SMVM), which is the main step of all iterative algorithms for solving ...
6
Distributed Matrix-Vector Multiplication with Sparsity and Privacy Guarantees
... Institute for Communications Engineering, Technical University of Munich, Munich, Germany {marvin.xhemrishi, rawad.bitar, antonia.wachter-zeh}@tum.de Abstract—We consider the problem of designing a coding scheme that ...
Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs
... symmetric matrix-vector multiplication (SYMV) – which is crucial for the performance of linear as well as eigen- problem solvers on symmetric ...generic matrix-vector ...
10
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 ...sparse matrix vector multiplication, the original plan has to be ...
86
Cache-oblivious sparse matrix-vector multiplication by using sparse matrix partitioning methods
... sparse matrix–vector ...input matrix using a recursive hypergraph-based sparse matrix partitioning scheme so that the resulting matrix induces cache-friendly behavior during sparse ...
27
Analysis of Partitioning Models and Metrics in Parallel Sparse Matrix-Vector Multiplication
... sparse matrix-vector multiplication (SpMxV) is one of the representative applications that renders these models and methods indispensable in many scientific com- puting ...
11
Performance Prediction Based on Statistics of Sparse Matrix Vector Multiplication on GPUs
... Sparse matrix-vector multiplication (SpMV) is an essential operation in solving linear systems and eigenvalue problems. For many iterative methods, the frac- tion of the execution time of SpMV may be ...
19
Parallel Multicore CSB Format and Its Sparse Matrix Vector Multiplication
... sparse matrix-vector multiplication (SpMV) is the most costly step in solving linear systems and eigenvalue problems. So improving the performance of the SpMV is often able to enhance the overall ...
8
Matrix-Vector Multiplication in Sub-Quadratic Time (Some Preprocessing Required)
... A preprocessing/multiplication algorithm for matrix-vector multiplication that builds on lookup table techniques. 20.[r] ...
55
Hypergraph-partitioning based decomposition for parallel sparse-matrix vector multiplication
... representation for the communication requirement than the standard computational graph model widely used in the literature for the parallelization of various scientific applications. The proposed models reduce the decom- ...
21
On analysis of partitioning models and metrics in parallel sparse matrix-vector multiplication
... the relative performances of the models do not change with K. However, their difference tend to increase and hence, the model used for partitioning becomes more important as the parallel matrix-vector ...
29
Cache locality exploiting methods and models for sparse matrix-vector multiplication
... sparse matrix-vector multiplication (SpMxV) is an important kernel operation widely used in linear ...sparse matrix is multiplied by a dense vec- tor repeatedly in these solvers to solve a ...
70
A methodology for speeding up matrix vector multiplication for single/multi core architectures
... Keywords Matrix Vector Multiplication · data cache · cache associativity · multi-core · SIMD · memory management · data reuse 1 Introduction The dense Matrix Vector ...
27
High Performance Asynchronous Pipelined QDI Templates for DCT Matrix vector Multiplication
... The mass application of asynchronous design has been an elusive goal for academic researchers while recent advances are promising. However asynchronous circuit has some inherent advantages over synchronous counterpart. ...
8
Adaptive Multi-level Blocking Optimization for Sparse Matrix Vector Multiplication on GPU
... Sparse matrix vector multiplication (SpMV) is the dominant kernel in scientific ...input vector causes further performance ...sparse matrix format called the Adaptive Multi-level ...
12
Optimising Sparse Matrix Vector multiplication for large scale FEM problems on FPGA
... Abstract—Sparse Matrix Vector multiplication (SpMV) is an important kernel in many scientific ...local matrix assembly ...diagonal matrix which is typically solved using an iterative ...
9
Generating Fast Sparse Matrix Vector Multiplication From a High Level Generic Functional IR
... Dense matrix vector multiplication The Lift project has shown how this high-level program can be transformed into high-performance code on a series of hardware architectures by exploring optimization ...
12
Sparse matrix-vector multiplication on GPGPUs
... sparse matrix in decreasing order of the number of nonzero elements; then, the rows are separated into blocks and each block is stored in the ELLPACK ...predict matrix-dependent tuning parameters, such as ...
62
Matrix-vector multiplication in terms of dot-products
... I The intensity value of a low-res pixel is the average of the intensity values of the corresponding high-res pixels.... Applications of dot-product definition of matrix-vector multiplic[r] ...
24