Agent, genetic algorithm with task duplication based scheduling technique for heterogenous systems
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Date
2018-08-16
Authors
Sidhu, Navdeep
Journal Title
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Abstract
High-Performance Computing (HPC) is used to solve complex problems in parallel for in-
creased performance. Over the past few years, parallelization has become more challenging
with the many core general purpose systems and accelerators. One of the challenges is
in better utilization of the resources available on these architectures through better task
scheduling strategies. In this thesis I consider a distributed, heterogeneous network with
general processing CPU based systems of varying speed and architectures. I propose an
efficient mapping and scheduling of tasks to processors using agents to explore the network
and Genetic Algorithm with Task Duplication Scheduling(GATDS) to schedule the tasks.
The SIPS (Serial algorithms In Parallel System) framework is used to exploit parallelism
using abstract syntax trees generated directly from the source code. This framework helps
in automating the process of converting serial code for use in parallel systems, thus reducing
the overhead of writing parallel code.
GATDS is compared with various scheduling strategies for task independent and task
dependent problems. The performance of GATDS is comparable to the use of existing
genetic algorithms for task independent problems. For inter-dependant tasks, the proposed
technique matches or performs better than the traditional Chunk scheduler and genetic
algorithm 75 % of the time. GATDS also provides better resource utilization.
Description
Keywords
GA - Genetic Algorithm, TDS - Task Duplication Strategy/Scheduling, SIPS - Serial Algorithms in Parallel System, GA-TDS - Genetic Algorithm with Task Duplication Scheduling, DAG - Directed Acyclic Graph, HPC - High Performance Computing
Citation
Sidhu, Navdeep Singh, Parimala Thulasiraman, and Ruppa Thulasiraman. "Agent, Genetic Algorithm with Task Duplication Based Scheduling Technique For Heterogenous Systems." Proceedings of the Annual Conference of the Administrative Sciences Association of Canada, Management Science Division, May 26-29, 2018, 43-81.