HeDPM: load balancing of linear pipeline applications on heterogeneous systems

HeDPM: load balancing of linear pipeline applications on heterogeneous systems This work presents a new algorithm, called Heterogeneous Dynamic Pipeline Mapping, that allows for dynamically improving the performance of pipeline applications running on heterogeneous systems. It is aimed at balancing the application load by determining the best replication (of slow stages) and gathering (of fast stages) combination taking into account processors computation and communication capacities. In addition, the algorithm has been designed with the requirement of keeping complexity low to allow its usage in a dynamic tuning tool. For this reason, it uses an analytical performance model of pipeline applications that addresses hardware heterogeneity and which depends on parameters that can be known in advance or measured at run-time. A wide experimentation is presented, including the comparison with the optimal brute force algorithm, a general comparison with the Binary Search Closest algorithm, and an application example with the Ferret pipeline included in the PARSEC benchmark suite. Results, matching those of the best existing algorithms, show significant performance improvements with lower complexity ( $$O(N^3$$ O ( N 3 ), where N is the number of pipeline stages). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Supercomputing Springer Journals

HeDPM: load balancing of linear pipeline applications on heterogeneous systems

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Publisher
Springer US
Copyright
Copyright © 2017 by The Author(s)
Subject
Computer Science; Programming Languages, Compilers, Interpreters; Processor Architectures; Computer Science, general
ISSN
0920-8542
eISSN
1573-0484
D.O.I.
10.1007/s11227-017-1971-4
Publisher site
See Article on Publisher Site

Abstract

This work presents a new algorithm, called Heterogeneous Dynamic Pipeline Mapping, that allows for dynamically improving the performance of pipeline applications running on heterogeneous systems. It is aimed at balancing the application load by determining the best replication (of slow stages) and gathering (of fast stages) combination taking into account processors computation and communication capacities. In addition, the algorithm has been designed with the requirement of keeping complexity low to allow its usage in a dynamic tuning tool. For this reason, it uses an analytical performance model of pipeline applications that addresses hardware heterogeneity and which depends on parameters that can be known in advance or measured at run-time. A wide experimentation is presented, including the comparison with the optimal brute force algorithm, a general comparison with the Binary Search Closest algorithm, and an application example with the Ferret pipeline included in the PARSEC benchmark suite. Results, matching those of the best existing algorithms, show significant performance improvements with lower complexity ( $$O(N^3$$ O ( N 3 ), where N is the number of pipeline stages).

Journal

The Journal of SupercomputingSpringer Journals

Published: Feb 2, 2017

References

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