Abstract
The development of efficient parallel algorithms for large scale wildfire simulations is a challenging research problem because the factors that determine wildfire behavior are complex; they include fuel characteristics and configurations, chemical reactions, balances between different modes of heat transfer, topography, and fire/atmosphere interactions. These factors make static parallel algorithms inefficient, especially when large number of processors are used because we cannot predict accurately the propagation of the fire and its computational requirements at runtime. In this paper, we present an Autonomic Runtime Manager (ARM) to dynamically exploit the physics properties of the fire simulation and use them as the basis of our self-optimization algorithm. At each step of the wildfire simulation, the ARM decomposes the computational domain into several natural regions (e.g., burning, unburned, burned) where each region has the same temporal and special characteristics. The number of burning, unburned and burned cells determines the current state of the fire simulation and can then be used to accurately predict the computational power required for each region. By regularly monitoring the state of the simulation and analyzing it, and use that to drive the runtime optimization, we can achieve significant performance gains because we can efficiently balance the computational load on each processor. Our experimental results show that the performance of the fire simulation has been improved by 45% when compared with a static portioning algorithm that does not take into considerations the state of the computations.
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References
Andrews, P.L.: BEHAVE: Fire Behavior Prediction and Fuel Modeling System - BURN Subsystem., Part 1. General Technical Report INT-194. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station; p. 130 (1986)
Rothermel, R.C.: A Mathematical Model for Predicting Fire Spread in Wildland Fuels., Research Paper INT-115. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; p. 40 (1972)
Anderson, H.E.: Predicting Wind-Driven Wildland Fire Size and Shape., Research Paper INT-305. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; p. 26 (1983)
Snir, M., Otto, S., Huss-Lederman, S., Walker, D.: MPI the Complete Reference. MIT Press, Cambridge (1996)
Hariri, S., Xue, L., Chen, H., et al.: AUTONOMIA: an autonomic computing environment. In: Conference Proc. of the 2003 IEEE IPCCC,
Hariri, S., Khargharia, B., Chen, H., Zhang, Y., Kim, B., Liu, H., Parashar, M.: The Autonomic Programming Paradigm. Submitted to IEEE computer (2004)
Crandall, P.E., Quinn, M.J.: Block Data Decomposition for Data-parallel Programming on a Heterogeneous Workstation Network. 2nd IEEE HPDC, 42–49 (1993)
Hu, Y.F., Blake, R.J.: Load Balancing for Unstructured Mesh Applications. Parallel and Distributed Computing Practices 2(3) (1999)
Ichikawa, S., Yamashita, S.: Static Load Balancing of Parallel PDE Solver for Distributed Computing Environment. Proc. 13th Int’l Conf. Parallel and Distributed Computing Systems, 399–405 (2000)
Cierniak, M., Zaki, M.J., Li, W.: Compile-Time Scheduling Algorithms for Heterogeneous Network of Workstations. Computer J. 40(6), 256–372 (1997)
Willebeek-LeMair, M., Reeves, A.P.: Strategies for Dynamic Load Balancing on Highly Parallel Computers. IEEE Trans. Parallel and Distributed Systems 4(9), 979–993 (1993)
Lin, F.C.H., Keller, R.M.: The Gradient Model Load Balancing Method. IEEE Trans. on Software Engineering 13(1), 32–38 (1987)
Cybenko, G.: Dynamic Load Balancing for Distributed Memory Multiprocessors. J. Parallel and Distributed Computing 7(2), 279–301 (1989)
Horton, G.: A Multi-Level Diffusion Method for Dynamic Load Balancing. Parallel Computing 19, 209–229 (1993)
Nedeljkovic, N., Quinn, M.J.: Data-Parallel Programming on a Network of Heterogeneous Workstations. 1st IEEE HPDC, 152–160 (September 1992)
Arabe, J., Beguelin, A., Lowekamp, B., Seligman, E., Starkey, M., Stephan, P.: Dome: Parallel Programming in a Heterogeneous Multi-User Environment. In: Proc. 10th Int’l Parallel Processing Symp., pp. 218–224 (1996)
Liu, C., Yang, L., Foster, I., Angulo, D.: Design and Evaluation of a Resource Selection Framework for Grid Applications. In: 11th IEEE HPDC, Edinburgh. Scotland (2002)
Wolski, R., Spring, N., Hayes, J.: The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing. Journal of Future Generation Computing Systems, 757–768 (1998)
Berman, F., Wolski, R., Figueria, S., Schopf, J., Shao, G.: Application-Level Scheduling on Distributed Heterogeneous Networks. Supercomputing 1996 (1996)
Berman, F., Wolski, R., Casanova, H., Cirne, W., Dail, H., Faerman, M., Figueira, S., Hayes, J., Obertelli, G., Schopf, J., Shao, G., Smallen, S., Spring, N., Su, A., Zagorodnov, D.: Adaptive Computing on the Grid Using AppLeS. IEEE Trans. on Parallel and Distributed Systems 14(4), 369–382 (2003)
Sun, X.-H., Wu, M.: Grid Harvest Service: A System for Long-Term, Application-Level Task Scheduling. In: Proc. of 2003 IEEE International Parallel and Distributed Processing Symposium (IPDPS 2003), Nice, France (April 2003)
Oliker, L., Biswas, R.: Plum: Parallel Load Balancing for Adaptive Unstructured Meshes. J. Parallel and Distributed Computing 52(2), 150–177 (1998)
Walshaw, C., Cross, M., Everett, M.: Parallel Dynamic Graph Partitioning for Adaptive Unstructured Meshes. J. Parallel and Distributed Computing 47, 102–108 (1997)
Zhang, Y., Yang, J., Chandra, S., Hariri, S., Parashar, M.: Autonomic Proactive Runtime Partitioning Strategies for SAMR Applications. In: Proceedings of the NSF Next Generation Systems Program Workshop, IEEE/ACM 18th International Parallel and Distributed Processing Symposium, Santa Fe, NM, USA, April 2004, page. 8 (2004)
Liu, H., Parashar, M., Hariri, S.: A Component-based Programming Framework for Autonomic Applications. In: Proceedings of the 1st IEEE International Conference on Autonomic Computing (ICAC 2004), May 2004, pp. 278–279. IEEE Computer Society Press, New York (2004)
Kephart, J.O., Chess, D.M.: The Vision of Autonomic Computing. IEEE Computer 36(1), 41–50 (2003)
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Yang, J., Chen, H., Kim, B.u., Hariri, S., Parashar, M. (2005). Autonomic Runtime System for Large Scale Parallel and Distributed Applications. In: Banâtre, JP., Fradet, P., Giavitto, JL., Michel, O. (eds) Unconventional Programming Paradigms. UPP 2004. Lecture Notes in Computer Science, vol 3566. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527800_23
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DOI: https://doi.org/10.1007/11527800_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27884-9
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