Abstract
Parallelization paradigms for effective execution in a Directed Acyclic Graph (DAG) application have been widely studied in the area of task scheduling. Schedule length can be varied depending on task assignment policies, scheduling policies, and heterogeneity in terms of each processor and each communication bandwidth in a heterogeneous system. One disadvantage of existing task scheduling algorithms is that the schedule length cannot be reduced for a data intensive application. In this paper, we propose a clustering-based task scheduling algorithm called Clustering for Minimizing the Worst Schedule Length (CMWSL) to minimize the schedule length in a large number of heterogeneous processors. First, the proposed method derives the lower bound of the total execution time for each processor by taking both the system and application characteristics into account. As a result, the number of processors used for actual execution is regulated to minimize the Worst Schedule Length (WSL). Then, the actual task assignment and task clustering are performed to minimize the schedule length until the total execution time in a task cluster exceeds the lower bound. Experimental results indicate that CMWSL outperforms both existing list-based and clustering-based task scheduling algorithms in terms of the schedule length and efficiency, especially in data-intensive applications.
| Original language | English |
|---|---|
| Article number | 7401062 |
| Pages (from-to) | 3144-3157 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Parallel and Distributed Systems |
| Volume | 27 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 2016 Nov 1 |
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Keywords
- DAG scheduling
- heterogeneous systems
- task clustering
- Task scheduling
ASJC Scopus subject areas
- Signal Processing
- Hardware and Architecture
- Computational Theory and Mathematics
Cite this
Clustering-Based Task Scheduling in a Large Number of Heterogeneous Processors. / Kanemitsu, Hidehiro; Hanada, Masaki; Nakazato, Hidenori.
In: IEEE Transactions on Parallel and Distributed Systems, Vol. 27, No. 11, 7401062, 01.11.2016, p. 3144-3157.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Clustering-Based Task Scheduling in a Large Number of Heterogeneous Processors
AU - Kanemitsu, Hidehiro
AU - Hanada, Masaki
AU - Nakazato, Hidenori
PY - 2016/11/1
Y1 - 2016/11/1
N2 - Parallelization paradigms for effective execution in a Directed Acyclic Graph (DAG) application have been widely studied in the area of task scheduling. Schedule length can be varied depending on task assignment policies, scheduling policies, and heterogeneity in terms of each processor and each communication bandwidth in a heterogeneous system. One disadvantage of existing task scheduling algorithms is that the schedule length cannot be reduced for a data intensive application. In this paper, we propose a clustering-based task scheduling algorithm called Clustering for Minimizing the Worst Schedule Length (CMWSL) to minimize the schedule length in a large number of heterogeneous processors. First, the proposed method derives the lower bound of the total execution time for each processor by taking both the system and application characteristics into account. As a result, the number of processors used for actual execution is regulated to minimize the Worst Schedule Length (WSL). Then, the actual task assignment and task clustering are performed to minimize the schedule length until the total execution time in a task cluster exceeds the lower bound. Experimental results indicate that CMWSL outperforms both existing list-based and clustering-based task scheduling algorithms in terms of the schedule length and efficiency, especially in data-intensive applications.
AB - Parallelization paradigms for effective execution in a Directed Acyclic Graph (DAG) application have been widely studied in the area of task scheduling. Schedule length can be varied depending on task assignment policies, scheduling policies, and heterogeneity in terms of each processor and each communication bandwidth in a heterogeneous system. One disadvantage of existing task scheduling algorithms is that the schedule length cannot be reduced for a data intensive application. In this paper, we propose a clustering-based task scheduling algorithm called Clustering for Minimizing the Worst Schedule Length (CMWSL) to minimize the schedule length in a large number of heterogeneous processors. First, the proposed method derives the lower bound of the total execution time for each processor by taking both the system and application characteristics into account. As a result, the number of processors used for actual execution is regulated to minimize the Worst Schedule Length (WSL). Then, the actual task assignment and task clustering are performed to minimize the schedule length until the total execution time in a task cluster exceeds the lower bound. Experimental results indicate that CMWSL outperforms both existing list-based and clustering-based task scheduling algorithms in terms of the schedule length and efficiency, especially in data-intensive applications.
KW - DAG scheduling
KW - heterogeneous systems
KW - task clustering
KW - Task scheduling
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UR - http://www.scopus.com/inward/citedby.url?scp=84994476443&partnerID=8YFLogxK
U2 - 10.1109/TPDS.2016.2526682
DO - 10.1109/TPDS.2016.2526682
M3 - Article
AN - SCOPUS:84994476443
VL - 27
SP - 3144
EP - 3157
JO - IEEE Transactions on Parallel and Distributed Systems
JF - IEEE Transactions on Parallel and Distributed Systems
SN - 1045-9219
IS - 11
M1 - 7401062
ER -