In this paper we describe a new approach for accelerating the Conjugate Gradient (CG) method using an FPGA co-processor. As in previous approaches, our co-processor performs a double-precision sparse matrix-vector multiplication. However, our implementation doubles the amount of computation per unit of input data by exploiting the symmetry of the input matrix and computing the upper and lower triangle of the input matrix in parallel. Using a Virtex-2 Pro 100 FPGA, we have achieved an observed computational throughput of 1155 MFLOPS
Matrix operations, like matrix multiplication, are commonly used in almost all areas of scientific r...
Different approaches are discussed for exploiting parallelism in the ICCG (Incomplete Cholesky Conju...
Solving a system of linear and nonlinear equations lies at the heart of many scientific and engineer...
In this paper we describe a new approach for accelerating the Conjugate Gradient (CG) method using a...
To find the solution to large dense systems have always been a very time consuming problem, this the...
UnrestrictedThe large capacity of field programmable gate arrays (FPGAs) has prompted researchers to...
In linear solvers, like the conjugate gradient algorithm, sparse-matrix vector multiplication is an ...
The Conjugate Gradient (CG) method is a widely-used iterative method for solving linear systems desc...
Conjugate gradient is an important iterative method used for solving least squares problems. It is c...
© 2014 Technical University of Munich (TUM).The conjugate gradient (CG) is one of the most widely us...
The sparse Matrix-Vector multiplication is a key operation in science and engineering along with th...
Abstract. The limiting factor for efficiency of sparse linear solvers is the memory bandwidth. In th...
Reconfigurable computing can significantly improve the performance and energy efficiency of many app...
AbstractWe propose a parallel implementation of the Preconditioned Conjugate Gradient algorithm on a...
Original article can be found at: http://www.medjcn.com/ Copyright Softmotor LimitedHigh performance...
Matrix operations, like matrix multiplication, are commonly used in almost all areas of scientific r...
Different approaches are discussed for exploiting parallelism in the ICCG (Incomplete Cholesky Conju...
Solving a system of linear and nonlinear equations lies at the heart of many scientific and engineer...
In this paper we describe a new approach for accelerating the Conjugate Gradient (CG) method using a...
To find the solution to large dense systems have always been a very time consuming problem, this the...
UnrestrictedThe large capacity of field programmable gate arrays (FPGAs) has prompted researchers to...
In linear solvers, like the conjugate gradient algorithm, sparse-matrix vector multiplication is an ...
The Conjugate Gradient (CG) method is a widely-used iterative method for solving linear systems desc...
Conjugate gradient is an important iterative method used for solving least squares problems. It is c...
© 2014 Technical University of Munich (TUM).The conjugate gradient (CG) is one of the most widely us...
The sparse Matrix-Vector multiplication is a key operation in science and engineering along with th...
Abstract. The limiting factor for efficiency of sparse linear solvers is the memory bandwidth. In th...
Reconfigurable computing can significantly improve the performance and energy efficiency of many app...
AbstractWe propose a parallel implementation of the Preconditioned Conjugate Gradient algorithm on a...
Original article can be found at: http://www.medjcn.com/ Copyright Softmotor LimitedHigh performance...
Matrix operations, like matrix multiplication, are commonly used in almost all areas of scientific r...
Different approaches are discussed for exploiting parallelism in the ICCG (Incomplete Cholesky Conju...
Solving a system of linear and nonlinear equations lies at the heart of many scientific and engineer...