Cholesky Decomposition and Linear Programming on a GPU
نویسندگان
چکیده
GPU ∗ † [Extended Abstract] Jin Hyuk Jung Department of Computer Science University of Maryland, College Park [email protected] Dianne P. O’Leary Department of Computer Science University of Maryland, College Park [email protected] ABSTRACT The rapid evolution of Graphics Processing Units (GPUs) in performance, architecture, and programmability provides computational potential beyond their primary purpose, graphics processing. In this work we present an efficient algorithm for solving symmetric and positive definite linear systems using triangular update on a GPU. Using the decomposition algorithm and other basic building blocks for linear algebra on the GPU, we demonstrate a GPU-powered linear program solver based on a Primal-Dual Interior-Point Method. Contributions: We present a new algorithm to decompose symmetric and positive definite dense matrices through a set of kernel calls with minimum copying operations to maximize performance. Using our algorithm and other BLAS kernels, we demonstrate how to build a GPU-powered primaldual interior-point method with minimal feedback to the CPU. We use:
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