The different methods used in the multiplication of numbers are long method (column method) multiplication, grid and addition multiplication. utilization mentioned to solve power (index) base number (number that multiply itself a times) horrendous, outrageous, tedious, time consuming too be carried out. This study develops computation using Kifilideen (matrix, combination distributive (MCD)) ap...

Matrix multiplication is a computationally-intensive and fundamental matrix operation in many algorithms used in scientific computations. It serves as the basic building block for signal, image processing, graphics and robotic applications. To improve the performance of these applications, a high performance matrix multiplier is required. Traditionally, matrix multiplication operation is either...

Sparse Matrix-Matrix multiplication (SpMM) is a fundamental operation over irregular data, which is widely used in graph algorithms, such as finding minimum spanning trees and shortest paths. In this work, we present a hybrid CPU and GPU-based parallel SpMM algorithm to improve the performance of SpMM. First, we improve data locality by element-wise multiplication. Second, we utilize the ordere...

In this paper, we give what we believe to be the rst high performance parallel implementation of Strassen's algorithm for matrix multiplication. We show how under restricted conditions, this algorithm can be implemented plug compatible with standard parallel matrix multiplication algorithms. Results obtained on a large Intel Paragon system show a 10-20% reduction in execution time compared to w...

This talk will describe recent progresses in the development of quantum algorithms for matrix multiplication. I will start with the case of Boolean matrices, and discuss the time complexity and query complexity of Boolean matrix multiplication in the quantum setting. I will then focus on other kinds of matrix products, in particular matrix products over algebraic structures known as semirings (...

ÐIn this paper, we address the issue of implementing matrix multiplication on heterogeneous platforms. We target two different classes of heterogeneous computing resources: heterogeneous networks of workstations and collections of heterogeneous clusters. Intuitively, the problem is to load balance the work with different speed resources while minimizing the communication volume. We formally sta...