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Author Coutinho, Jose G.F. ♦ Hmid, Soukaina N. ♦ Luk, Wayne
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Abstract This paper presents a novel resource management approach for efficiently managing the computation and the data movements between the host and its accelerators in a heterogeneous platform. Our approach is based on OmpSs, with support for multi-core CPUs, GPGPUs and Maxeler Data Flow Engines based on FPGA technology; it exploits data locality, data transfer costs and data dependencies. The proposed approach is supported by an offline learning process coupled with online monitoring, allowing performance to be estimated while learning from past observations during execution. Its performance is compared against the current OmpSs scheduler using five benchmarks: matrix multiplication, bitonic sort, N-body simulation, Cholesky decomposition and AdPredictor. The results show the proposed approach can achieve up to 4.25 times speed-up for Cholesky decomposition. Moreover, an evaluation with AdPredictor indicates that the FPGA version is up to 46 times faster than the CPU version for large task sizes.
Description Affiliation: Imperial College London, United Kingdom (Hmid, Soukaina N.; Coutinho, Jose G.F.; Luk, Wayne)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1981-04-01
Publisher Place New York
Journal ACM SIGARCH Computer Architecture News (CARN)
Volume Number 43
Issue Number 4
Page Count 6
Starting Page 40
Ending Page 45


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Source: ACM Digital Library