Abstract
Energy-efficient design of multimedia embedded systems demands optimizations in both hardware and software. Software optimization has no received much attention, although modern multimedia applications exhibit high resource utilization. In order to efficiently run this kind of applications in embedded systems, the dynamic memory subsystem needs to be optimized. A key role in this optimization is played by the Dynamic Data Types (DDTs) that reside in every real-life application. It would be desirable to organize this set of DDTs to achieve the best performance in the target embedded system. This problem is NP-complete, and cannot be fully explored. In these cases the use of parallel processing can be very useful because it allows not only to explore more solutions spending the same time, but also to implement new algorithms. In this work, we propose a method that uses parallel processing and evolutionary computation to explore DDTs in the design of embedded applications. We propose a parallel Multi-Objective Evolutionary Algorithm (MOEA) which combines NSGA-II and SPEA2. We use Discrete Event Systems Specification (DEVS) to implement this parallel evolutionary algorithm over Service Oriented Architecture (SOA). Parallelism improves the solutions found by the corresponding sequential algorithms, and it allows system designers to reach better solutions than previous approximations.
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References
Arizona center of integrative modeling & simulation, acims (2008), http://www.acims.arizona.edu
Antonakos, J.L., Mansfield, K.C.: Practical Data Structures using C/C++. Prentice-Hall, Englewood Cliffs (1999)
Atienza, D., Baloukas, C., Papadopoulos, L., Poucet, C., Mamagkakis, S., Hidalgo, J.I., Catthoor, F., Soudris, D., Lanchares, J.: Optimization of dynamic data structures in multimedia embedded systems using evolutionary computation. In: SCOPES 2007: Proceedingsof the 10th international workshop on Software & compilers for embedded systems, pp. 31–40. ACM Press, New York (2007), http://doi.acm.org/10.1145/1269843.1269849
Benini, L., de Micheli, G.: System-level power optimization: techniques and tools. ACM Trans. Des. Autom. Electron. Syst. 5(2), 115–192 (2000), http://doi.acm.org/10.1145/335043.335044
Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Dordrecht (2000)
Catthoor, F., Danckaert, K., Kulkarni, C., Brockmeyer, E., Kjeldsberg, P.G., Achteren, T.V., Omnes, T.: Data access and storage management for embedded programmable processors. Kluwer Academic Publishers, Dordrecht (2002)
Choi, Y., Kim, T., Han, H.: Memory layout techniques for variables utilizing efficient dram access modes in embedded system design. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 24(2), 278–287 (2005)
Coello, C.: A comparative survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems 1, 269–308 (1999)
Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J.: Pesa-ii: Region-based selection in evolutionary multiobjective optimization. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 283–290. Morgan Kaufmann, San Francisco (2001)
Daylight, E.G., Atienza, D., Vandecappelle, A., Catthoor, F., Mendias, J.M.: Memory-access-aware data structure transformations for embedded software with dynamic data accesses. IEEE Transactions on VLSI Systems 12, 269–280 (2004)
Deb, K.: Multiobjective Optimization using Evolutionary Algorithms. John Wiley and Son Ltd., Chichester (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Edler, J.: Dinero iv trace-driven uniprocessor cache simulator (2008), http://pages.cs.wisc.edu/~markhill/DineroIV
Fernandez, J.M., Vila, P., Calle, E., Marzo, J.L.: Design of virtual topologies using the elitist team of multiobjective evolutionary algorithms. In: Obaidat, M., Gburzynski, P. (eds.) Proceedings of International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2007), San Diego, USA, pp. 266–271 (2007)
Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA 1993), pp. 416–423 (1993)
Hajela, P., Lin, C.Y.: Genetic search strategies in multicriterion optimal design. Structural Opt. 4, 99–107 (1992)
Hardee, K., Jones, F., Butler, D., Parris, M., Mound, M., Calendar, H., Jones, G., Aldrich, L., Gruenschlaeger, C., Miyabayashil, M., Taniguchi, K., Arakawa, I.: A 0.6v 205mhz 19.5ns trc 16mb embedded dram. In: IEEE International Solid-State Circuits Conference, ISSCC (2004)
Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, vol. 1, pp. 82–87 (1994)
Kharevych, L., Khan, R.: 3d physics engine for elastic and deformable bodies. University of Maryland, College Park (2002), http://www.cs.umd.edu/Honors/reports/kharevych.html
Michalewicz, Z.: Genetic Algorithms + data structures = Evolution Programs. Springer, Heidelberg (1996)
Mittal, S., Risco-Martin, J.L., Zeigler, B.P.: Devs/soa: A cross-platform framework for net-centric modeling and simulation using devs. Submitted to SIMULATION: Transactions of SCS, in review (2007)
Mittal, S., Risco-Martín, J.L., Zeigler, B.P.: Devs-based web services for net-centric t&e. In: Summer Computer Simulation Conference, SCSC 2006 (2006)
Muttreja, A., Raghunathan, A., Ravi, S., Jha, N.K.: Automated energy/performance macromodeling of embedded software. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 26(3), 542–552 (2007)
Panda, P.R., Catthoor, F., Dutt, N.D., Danckaert, K., Brockmeyer, E., Kulkarni, C., Vandercappelle, A., Kjeldsberg, P.G.: Data and memory optimization techniques for embedded systems. ACM Trans. Des. Autom. Electron. Syst. 6(2), 149–206 (2001), http://doi.acm.org/10.1145/375977.375978
Risco-Martin, J.L., Atienza, D., Hidalgo, J.I., Lanchares, J.: Analysis of multi-objective evolutionary algorithms to optimize dynamic data types in embedded systems. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008 (2008)
Risco-Martin, J.L., Atienza, D., Hidalgo, J.I., Lanchares, J., Mittal, S.: Optimization of multimedia embedded applications using genetic algorithms and discrete event simulation over soa. Submitted to IEEE Transactions on Computer-Aided Design
Risco-Martín, J.L., Atienza, D., Hidalgo, J.I., Lanchares, J.: A parallel evolutionary algorithm to optimize dynamic data types in embedded systems. Soft Computing - A Fusion of Foundations, Methodologies and Applications 12(12), 1157–1167 (2008)
Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pp. 93–100. Hillsdale, New Jersey (1985)
Shivakumar, P., Jouppi, N.P.: Cacti 3.0: An integrated cache timing, power, and area model. Tech. Rep. 2001/2, Compaq Computer Corporation (2001)
de Toro Negro, F., Ortega, J., Ros, E., Mota, S., Paechter, B., Martín, J.: Psfga: Parallel processing and evolutionary computation for multiobjective optimisation. Parallel Computing 30(5-6), 721–739 (2004)
Veldhuizen, D.A.V., Zydallis, J.B., Lamont, G.B.: Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7(2), 144–173 (2003)
Wilson, L., Moore, M.: Cross-pollinating parallel genetic algorithms for multiobjective search and optimization. International Journal of Foundations of Computer Science 16(2), 261–280 (2005)
Wuytack, S., Catthoor, F., De Man, H.: Transforming set data types to power optimal data structures. IEEE Transactions on Computer-Aided Design 15(6), 619–629 (1996)
Xiong, S., Li, F.: Parallel strength pareto multi-objective evolutionary algorithm for optimization problems. In: Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), vol. 4, pp. 2712–2718. IEEE Press, Canberra (2003)
Zeigler, B.P., Kim, T., Praehofer, H.: Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems. Academic Press, London (2000)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Proceedings of the Evolutionary Methods for Design, Optimization and Control with Application to Industrial Problems, Barcelona, Spain, pp. 95–100 (2002)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computing 3(4), 257–271 (1998)
Zydallis, J.B., Van Veldhuizen, D.A., Lamont, G.B.: A statistical comparison of multiobjective evolutionary algorithms including the MOMGA-II. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 226–240. Springer, Heidelberg (2001)
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Risco-Martín, J.L., Atienza, D., Hidalgo, J.I., Lanchares, J. (2010). Parallel and Distributed Optimization of Dynamic Data Structures for Multimedia Embedded Systems. In: de Vega, F.F., Cantú-Paz, E. (eds) Parallel and Distributed Computational Intelligence. Studies in Computational Intelligence, vol 269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10675-0_12
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