NDLI: Solving optimization problems using a hybrid systolic search on GPU plus CPU

Content Provider	Springer Nature Link
Author	Vidal, Pablo Alba, Enrique Luna, Francisco
Copyright Year	2016
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 $$365\times $$ 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.
Starting Page	3227
Ending Page	3245
Page Count	19
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	21
Issue Number	12
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-01-11
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	GPGPU CPU–GPU cooperative algorithm Optimization Heterogeneous architectures Parallel hybrid algorithms Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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