fast matrix multiplicationFast matrix multiplication (FMM) algorithms to multiply two n /spl times/ n matrices reduce the asymptotic operation count from O(n/sup 3/) of the traditional algorithm to O(n/sup 2.38/), thus on distributed memory computers, the association of FMM algorithms and the...
Matrix multiplication is an important kernel in linear algebra algorithms, and the performance of both serial and parallel implementations is highly depend... S Chatterjee,AR Lebeckt,PK Patnala,... - ACM 被引量: 186发表: 1999年 A Fast Scalable Universal Matrix Multiplication Algorithm on Distribu...
For m ≤ n 1.14 , the new algorithm performs an almost optimal number of only n 2 + o (1) operations. For m ≤ n 1.68 , the new algorithm is also faster than the best known matrix multiplication algorithm for dense matrices which uses O ( n 2.38 ) algebraic operations. The ...
A. Fast algorithm for matrix- vector multiply of asymmetric multilevel block-toeplitz atrices. Antennas and Propagation Society International Symposium, IEEE. ... BE Barrowes,FL Teixeira,JA Kong - 《Microwave & Optical Technology Letters》 被引量: 111发表: 2001年 GPU-accelerated preconditioned iterat...
Estimation of the weighted mean and covariance matrix using an online algorithm (Clarke, 1971). Computation of central moments up to fourth order using an online algorithm (Spicer, 1972). Fast computation of Hadamard product using unrolled loops. ...
13 Commits doc src .gitignore LICENSE README.md README MIT license mtimesx Fast Matrix Multiply with Multi-Dimensional Support MTIMESXis a fast general purpose matrix and scalar multiply routine that has the following features: Supports multi-dimensional (nD, n>2) arrays directly ...
The fast multipole method (FMM) has been implemented to speed up the matrixvector multiply when an iterative method is used to solve combined field integral equation (CFIE). FMM reduces the complexity from O(N 2 ) to O(N 1:5 ). With a multilevel fast multipole algorithm (MLFMA), it ...
identify opportunities on commodity hardware for a single-wide (≥8b) multiply to compute a dot product of two vectors with multiple narrow (<8b) elements propose ULPPACK, an efficient implementation of sub-8-bit GEMM (General Matrix Multiplication) computation, by leveraging efficient packing and...
For nD cases, the first two dimensions specify the matrix multiply involved. The remaining dimensions are duplicated and specify the number of individual matrix multiplies to perform for the result. i.e., MTIMESX treats these cases as arrays of 2D matrices and performs the operation...
Fast and Scalable Matrix Multiply using spark, breeze and BLAS libraries - GitHub - shantanu-93/scalable-matrix-multiply: Fast and Scalable Matrix Multiply using spark, breeze and BLAS libraries