Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-6266
Main Title: Automatic problem size sensitive task partitioning on heterogeneous parallel systems
Author(s): Grasso, Ivan
Kofler, Klaus
Cosenza, Biagio
Fahringer, Thomas
Type: Article
Language Code: en
Abstract: In this paper we propose a novel approach which automatizes task partitioning in heterogeneous systems. Our framework is based on the Insieme Compiler and Runtime infrastructure. The compiler translates a single-device OpenCL program into a multi-device OpenCL program. The runtime system then performs dynamic task partitioning based on an offline-generated prediction model. In order to derive the prediction model, we use a machine learning approach that incorporates static program features as well as dynamic, input sensitive features. Our approach has been evaluated over a suite of 23 programs and achieves performance improvements compared to an execution of the benchmarks on a single CPU and a single GPU only.
URI: https://depositonce.tu-berlin.de//handle/11303/6927
http://dx.doi.org/10.14279/depositonce-6266
Issue Date: 2013
Date Available: 24-Oct-2017
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): code analysis
compilers
gpu
heterogeneous computing
machine learning
runtime system
task partitioning
Usage rights: Terms of German Copyright Law
Journal Title: ACM SIGPLAN Notices
Publisher: Association for Computing Machinery (ACM)
Publisher Place: New York, NY
Volume: 48
Issue: 8
Publisher DOI: 10.1145/2442516.2442545
10.1145/2517327.2442545
Page Start: 281
Page End: 282
ISBN: 978-1-4503-1922-5
ISSN: 0362-1340
Appears in Collections:Fachgebiet Architektur eingebetteter Systeme » Publications

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