Title: CUDA GPU libraries and novel sparse matrix-vector multiplication - implementation and performance enhancement in unstructured finite element computations

Authors: Richard Haney; Ram Mohan

Addresses: The MITRE Corporation, 7515 Colshire Drive, McLean, Virginia, 22102-7539, USA ' North Carolina A&T State University, 2907 East Gate City Blvd., Greensboro, North Carolina, 27401, USA

Abstract: The efficient solution to systems of linear and nonlinear equations arising from sparse matrix operations is a ubiquitous challenge for computing applications that can be exacerbated by the employment of heterogeneous architectures such as CPU-GPU computing systems. This paper presents our implementation of a novel sparse matrix-vector multiplication (a significant compute load operation in the iterative solution via pre-conditioned conjugate gradient based methods) employing LightSpMV with compressed sparse row (CSR) format, and the resulting performance characteristics using an unstructured finite element-based computational simulation. Computational performance analysed indicates that LightSpMV can provide an asset to boost performance for these computational modelling applications. This work also investigates potential improvements in the LightSpMV algorithm using CUDA 35 intrinsic, which results in an additional performance boost by 1%. While this may not be significant, it supports the idea that LightSpMV can potentially be used for other full-solution finite element-based computational implementations.

Keywords: general purpose GPU computing; GPGPU; sparse matrix-vector; finite element method; FEM; Compute Unified Device Architecture; CUDA; performance analysis.

DOI: 10.1504/IJCSE.2019.104436

International Journal of Computational Science and Engineering, 2019 Vol.20 No.4, pp.501 - 507

Received: 15 Feb 2017
Accepted: 23 Aug 2017
Published online: 12 Jan 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article