Randomized GPU Algorithms for the Construction of Hierarchical Matrices from Matrix-Vector Operations

Fast construction of hierarchical matrix representation from matrix-vector multiplication

We develop a hierarchical matrix construction algorithm using matrix–vector multiplications, based on the randomized singular value decomposition of low-rank matrices. The algorithm uses OðlognÞ applications of the matrix on structured random test vectors and Oðn lognÞ extra computational cost, where n is the dimension of the unknown matrix. Numerical examples on constructing Green’s functions ...

gProximity: Hierarchical GPU-based Operations for Collision and Distance Queries

We present novel parallel algorithms for collision detection and separation distance computation for rigid and deformable models that exploit the computational capabilities of many-core GPUs. Our approach uses thread and data parallelism to perform fast hierarchy construction, updating, and traversal using tight-fitting bounding volumes such as oriented bounding boxes (OBB) and rectangular swep...

construction of vector fields with positive lyapunov exponents

in this thesis our aim is to construct vector field in r3 for which the corresponding one-dimensional maps have certain discontinuities. two kinds of vector fields are considered, the first the lorenz vector field, and the second originally introced here. the latter have chaotic behavior and motivate a class of one-parameter families of maps which have positive lyapunov exponents for an open in...

Sparse Matrix-Vector Multiplication on a GPU

Sparse Matrix-vector Multiplication on Nvidia Gpu

In this paper, we present our work on developing a new matrix format and a new sparse matrix-vector multiplication algorithm. The matrix format is HEC, which is a hybrid format. This matrix format is efficient for sparse matrix-vector multiplication and is friendly to preconditioner. Numerical experiments show that our sparse matrix-vector multiplication algorithm is efficient on