https://github.com/hackerkid/heft-scheduling

Performance-effective and low-complexity task scheduling for heterogeneous computing
https://github.com/hackerkid/heft-scheduling

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Performance-effective and low-complexity task scheduling for heterogeneous computing

• Host: GitHub
• URL: https://github.com/hackerkid/heft-scheduling
• Owner: hackerkid
• Created: 2015-04-16T22:09:30.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2016-01-09T05:27:12.000Z (almost 9 years ago)
• Last Synced: 2023-08-01T12:24:42.700Z (over 1 year ago)
• Language: C++
• Homepage:
• Size: 3.91 KB
• Stars: 9
• Watchers: 3
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# HEFT-Scheduling Algorithm 

C++ implemntation of Performance-effective and low-complexity task scheduling for heterogeneous computing

##About

Efficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The 

application scheduling problem has been shown to be NP-complete in general cases as well as in several restricted cases.

Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the 

literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature 

for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality

schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous 

processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the

Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm. The HEFT algorithm 

selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which

minimizes its earliest finish time with an insertion-based approach. On the other hand, the CPOP algorithm uses the summation 

of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which 

schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to 

provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted

directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the 

graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of

both quality and cost of schedules, which are mainly presented with schedule length ratio, speedup, frequency of best results,

and average scheduling time metrics.

##Research Paper

[Link](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=993206&tag=1)

###Authors

* Topcuoglu, H - Comput. Eng. Dept., Marmara Univ., Istanbul, Turkey 

* Hariri, S. - Min-You Wu