Paper Info
Title
Solving optimization problems using a hybrid systolic search on GPU plus CPU
Abstract
In recent years, graphics processing units (GPUs) have emerged as a powerful architecture for solving a broad spectrum of applications in very short periods of time. However, most existing GPU optimization approaches do not exploit the full power available in a CPU---GPU platform. They have a tendency to leave one of them partially unused (usually the CPU) and fail to establish an accurate exchange of information that could help solve the target problem efficiently. Thus, better performance is expected from devising a hybrid CPU---GPU parallel algorithm that combines the highly parallel stream processing power of GPUs with the higher power of multi-core architectures. We have developed a hybrid methodology to efficiently solve optimization problems. We use a hybrid CPU---GPU architecture, to benefit from running it, in parallel, on both the CPU and the GPU. Our experiments over a heterogeneous set of combinatorial optimization problems with increasing dimensionality show a time gain of up to $$365\\times $$365× in our proposal, while demonstrating high numerical accuracy. This work is intended to open up a new line of research that matches both architectures with new algorithms and cooperation techniques.
Year
DOI
Venue
2017
10.1007/s00500-015-2005-x
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Keywords
Field
DocType
GPGPU, CPU–GPU cooperative algorithm, Optimization, Heterogeneous architectures, Parallel hybrid algorithms
Graphics,Central processing unit,Parallel algorithm,Computer science,Parallel computing,Curse of dimensionality,Theoretical computer science,Exploit,General-purpose computing on graphics processing units,Stream processing,Optimization problem
Journal
Volume
Issue
ISSN
21
12
1432-7643
Citations 
PageRank 
References 
6
0.47
33
Authors
3
Name
Order
Citations
PageRank
Pablo Vidal1452.81
Enrique Alba23796242.34
Francisco Luna314412.40