Date of Award

2021-08-01

Degree Name

Doctor of Philosophy

Department

Computational Science

Advisor(s)

Shirley V. Moore

Abstract

Sparse problems arise from a variety of applications, from scientific simulations to graph analytics. Traditional HPC systems have failed to effectively provide high bandwidth for sparse problems. This limitation is primarily because of the nature of sparse computations and their irregular memory access patterns.We predict the performance of sparse computations given an input matrix and GPU hardware characteristics. This prediction is done by identifying hardware bottlenecks in modern NVIDIA GPUs using roofline trajectory models. Roofline trajectory models give us insight into the performance by simultaneously showing us the effects of strong and weak scaling. We then create regression models for our benchmarks to model performance metrics. The outputs of these models are compared against empirical results. We expect our results to be useful to application developers in understanding the performance of their sparse algorithms in GPUs and to hardware designers in fine-tuning GPU features to better meet the requirements of sparse applications.

Language

en

Provenance

Recieved from ProQuest

File Size

125 p.

File Format

application/pdf

Rights Holder

Rogelio Long

Share

COinS