NDLI: Optimization of the HEFT Algorithm for a CPU-GPU Environment

Content Provider	IEEE Xplore Digital Library
Author	Shetti, K.R. Fahmy, S.A. Bretschneider, T.
Copyright Year	2013
Description	Author affiliation: Innovation Works, Eur. Aeronaut. Defense & Space Co., Singapore, Singapore (Bretschneider, T.) \|\| Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore (Shetti, K.R.; Fahmy, S.A.)
Abstract	Scheduling applications efficiently on a network of computing systems is crucial for high performance. This problem is known to be NP-Hard and is further complicated when applied to a CPU-GPU heterogeneous environment. Heuristic algorithms like Heterogeneous Earliest Finish Time (HEFT) have shown to produce good results for other heterogeneous environments like Grids and Clusters. In this paper, we propose a novel optimization of this algorithm that takes advantage of dissimilar execution times of the processors in the chosen environment. We optimize both the task ranking as well as the processor selection steps of the HEFT algorithm. By balancing the locally optimal result with the globally optimal result, we show that performance can be improved significantly without any change in the complexity of the algorithm (as compared to HEFT). Using randomly generated Directed A cyclic Graphs (DAGs), the new algorithm HEFT-NC (No-Cross) is compared with HEFT both in terms of speedup and schedule length. We show that the HEFT-NC outperforms HEFT algorithm and is consistent across different graph shapes and task sizes.
Starting Page	212
Ending Page	218
File Size	642115
Page Count	7
File Format	PDF
e-ISBN	9781479924196
DOI	10.1109/PDCAT.2013.40
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-16
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Program processors Schedules Processor scheduling Scheduling Complexity theory Measurement Clustering algorithms Heterogeneous GPGPU
Content Type	Text
Resource Type	Article

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