We are presenting a new method and algorithm for solving several common problems of linear algebra and optimization, where only vectors multiplication is used (matrices inversion and division is absent). The solution of these problems is reduced to consequently performed multiplication of a matrix by a vector. It leads to significant simplification of the corresponding programs for multi-core processors, and the solution time is very much reduced
In this report, we consider a simple but important linear algebra kernel, matrix-matrix multiplicati...
The implementations of matrix multiplication on contemporary, vector-oriented, and multicore-oriente...
This paper describes an approach for the automatic generation and optimization of numerical softwar...
We are presenting a new method and algorithm for solving several common problems of linear algebra a...
Multi-core processor is a new type of parallel nonlinear function. It has the characteristics of hig...
Two issues in linear algebra algorithms for multicomputers are addressed. First, how tounify paralle...
AbstractIn this article, we present a fast algorithm for matrix multiplication optimized for recent ...
International audienceIn this paper, a new methodology for computing the Dense Matrix Vector Multipl...
AbstractWe consider the problem of finding a basic solution to a system of linear constraints (in st...
Achieving high-performance while reducing power consumption is the key question as tech-nology scali...
Multiple independent matrix problems of very small size appear in a variety of different fields. In ...
We consider a multiple objective linear program (MOLP) max{Cx|Ax = b,x in N_{0}^{n}} where C = (c_ij...
We survey general techniques and open problems in numerical linear algebra on parallel architectures...
Abstract: Few realize that, for large matrices, many dense matrix computations achieve nearly the sa...
This thesis describes novel techniques and test implementations for optimizing numerically intensive...
In this report, we consider a simple but important linear algebra kernel, matrix-matrix multiplicati...
The implementations of matrix multiplication on contemporary, vector-oriented, and multicore-oriente...
This paper describes an approach for the automatic generation and optimization of numerical softwar...
We are presenting a new method and algorithm for solving several common problems of linear algebra a...
Multi-core processor is a new type of parallel nonlinear function. It has the characteristics of hig...
Two issues in linear algebra algorithms for multicomputers are addressed. First, how tounify paralle...
AbstractIn this article, we present a fast algorithm for matrix multiplication optimized for recent ...
International audienceIn this paper, a new methodology for computing the Dense Matrix Vector Multipl...
AbstractWe consider the problem of finding a basic solution to a system of linear constraints (in st...
Achieving high-performance while reducing power consumption is the key question as tech-nology scali...
Multiple independent matrix problems of very small size appear in a variety of different fields. In ...
We consider a multiple objective linear program (MOLP) max{Cx|Ax = b,x in N_{0}^{n}} where C = (c_ij...
We survey general techniques and open problems in numerical linear algebra on parallel architectures...
Abstract: Few realize that, for large matrices, many dense matrix computations achieve nearly the sa...
This thesis describes novel techniques and test implementations for optimizing numerically intensive...
In this report, we consider a simple but important linear algebra kernel, matrix-matrix multiplicati...
The implementations of matrix multiplication on contemporary, vector-oriented, and multicore-oriente...
This paper describes an approach for the automatic generation and optimization of numerical softwar...