ScholarLens Back to search
2018•IEEE Latin America TransactionsRequires access

An Efficient Parallel Implementation of an Optimized Simplex Method in GPU-CUDA

Vinicius O Silva, Carlos Augusto Paiva da Silva Martins, Petr Ekel

Open publisher page 2 citations

Abstract

The general-purpose graphics processing unit programming has been widely used and has achieved satisfactory results across many knowledge areas and it keeps evolving since its earlier stages. This paper presents an implementation of a modified Simplex method using the computational power brought from graphics processing unit (GPU) computing using the Nvidia framework for GPU programming, compute unified device architecture (CUDA). The results achieved shown that the parallel GPU implementation reached 15× and 22× maximum speedup measuring the overall time (data transfer plus kernel work time) and the kernel computation time respectively, in comparison with the standard sequential implementation using central processing unit (CPU).

About this research paper

What this paper is about

The general-purpose graphics processing unit programming has been widely used and has achieved satisfactory results across many knowledge areas and it keeps evolving since its earlier stages. This paper presents an implementation of a modified Simplex method using the computational power brought from graphics processing unit (GPU) computing using the Nvidia framework for GPU programming, compute unified device architecture (CUDA). The results achieved shown that the parallel GPU implementation reached 15× and 22× maximum speedup measuring the overall time (data transfer plus kernel work time) and the kernel computation time respectively, in comparison with the standard sequential implementation using central processing unit (CPU).

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

The general-purpose graphics processing unit programming has been widely used and has achieved satisfactory results across many knowledge areas and it keeps evolving since its earlier stages. This paper presents an implementation of a modified Simplex method using the computational power brought from graphics processing unit (GPU) computing using the Nvidia framework for GPU programming, compute unified device architecture (CUDA). The results achieved shown that the parallel GPU implementation reached 15× and 22× maximum speedup measuring the overall time (data transfer plus kernel work time) and the kernel computation time respectively, in comparison with the standard sequential implementation using central processing unit (CPU).

Key concepts: CUDA, Computer science, Graphics processing unit, Speedup, Parallel computing, General-purpose computing on graphics processing units, Kernel (algebra), Coprocessor

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
An Efficient Parallel Implementation of an Optimized Simplex Method in GPU-CUDA — Research Paper | ScholarLens