Abstract
In a distributed system, which consists of an unknown number of processors, it is important to derive an appropriate number of processors by which the good schedule length is obtained by a task scheduling. Many task clustering heuristics have been proposed to determine the number of processors and to minimize the schedule length for scheduling a directed acyclic graph (DAG) application. However, those heuristics are not aware of the actual number of existing processors. As a result, the number of processors determined by an existing task clustering may exceed that of actually existing processors. Therefore, conventional approaches adopt merging of each cluster for reducing the number of clusters at the expense of decreasing degree of task parallelism. In this paper, we present a static cluster size determination method, which derives the lower bound of the cluster size with considering the DAG structure and the task size to data size ratio to suppress the schedule length with the small number of processors. Our experimental evaluations by simulations show that the lower bound of each cluster size determined by the proposed method has a good impact on both the schedule length and the processor utilization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
At the initial DAG, each cluster includes only one task. Thus, LV s (i) for each cls s (i) in the initial DAG is the maximum path length from a START task to an END task, including cls s (i) = { n i s}. Therefore, WSL 0 equals to critical path length of the initial DAG.
References
Yang T, Gerasoulis A (1994) DSC: Scheduling parallel tasks on an unbounded number of processors. IEEE Trans Parallel Distr Syst 5(9):951–967
Liou JC, Palis MA (1996) An efficient task clustering heuristic for scheduling DAGs on multiprocessors. In: Proceedings of the 8th Symposium on Parallel and Distributed Processing, October 1996
Yang T, Gerasoulis A (1993) List scheduling with and without communication delays. Parallel Comput 19(12):1321–1344
Sarkar V (1989) Partitioning and scheduling parallel programs for execution on multiprocessors. MIT, MA
Gerasoulis A, Yang T (1992) A comparison of clustering heuristics for scheduling directed acyclic graphs on multiprocessors. J Parallel Distr Comput 16:276–291
Yang T, Gerasoulis A (1992) PYRROS: Static scheduling and code generation for message passing multiprocessors. In: Proceedings of the 6th ACM International Conference on Supercomputing, pp 428–437, 1992
Liou JC, Palis MA (1997) A comparison of general approaches to multiprocessor scheduling. In: Proceedings of the 11th International Symposium on Parallel Processing, pp 152–156, 1997
Gerasoulis A, Yang T (1993) On the granularity and clustering of directed acyclic task graphs. IEEE Trans Parallel Distr Syst 4(6):686–701
Wu MY, Gajski DD (1990) Hypertool: A programming aid for message-passing systems. IEEE Trans Parallel Distr Syst 1(3):330–343
Zomaya AY, Chan G (2004) Efficient clustering for parallel tasks execution in distributed systems. In: Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS’04), pp 167–175, Santa Fe, NM, USA, April 2004
Sinnen O, Sousa LA (2005) Communication contention in task scheduling. IEEE Trans Parallel Distr Syst 16(6):503–515
Sinnen O (2007) Task scheduling for parallel systems. Wiley, NY
Kanemitsu H, Lu Y, Otani Y, Lee G, Nakazato H, Hoshiai T, Urano Y (2008) A task clustering minimizing worst schedule length in distributed processing environment. IEICE Technical Reports, vol 108, no 361, pp 13–18, December 2008
Daoud MI, Kharma N (2008) A high performance algorithm for static task scheduling in heterogeneous distributed computing systems. J Parallel Distr Comput 68(4):399–409
Topcuoglu H, Hariri SH, Wu HY (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distr Syst 13(3):260–274
Cosnard M, Lorraine LI, Jeannot E, Yang T (1999) SLC: Symbolic scheduling for executing parameterized task graphs on multiprocessors. In: Proceedings of the International Conference on Parallel Processing (ICPP’99), pp 413–421, 1999
Amoura AK, Bampis E, Konig J-C (1998) Scheduling algorithms for parallel Gaussian elimination with communication costs. IEEE Trans Parallel Distr Syst 9(7):679–686
Chabridon S, Gelenbe E (1995) Dependable parallel computing with agents based on a task graph model. In: Proceedings of the 3rd Euromicro Workshop on Parallel and Distributed Processing, p 350, 1995
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer New York
About this chapter
Cite this chapter
Kanemitsu, H., Lee, G., Nakazato, H., Hoshiai, T., Urano, Y. (2011). Static Task Cluster Size Determination in Homogeneous Distributed Systems. In: Naono, K., Teranishi, K., Cavazos, J., Suda, R. (eds) Software Automatic Tuning. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6935-4_14
Download citation
DOI: https://doi.org/10.1007/978-1-4419-6935-4_14
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-6934-7
Online ISBN: 978-1-4419-6935-4
eBook Packages: EngineeringEngineering (R0)