Abstract
In today’s computer architectures the design spaces are huge, thus making it very difficult to find optimal configurations. One way to cope with this problem is to use Automatic Design Space Exploration (ADSE) techniques. We developed the Framework for Automatic Design Space Exploration (FADSE) which is focused on microarchitectural optimizations. This framework includes several state-of-the art heuristic algorithms.
In this paper we selected three of them, NSGA-II and SPEA2 as genetic algorithms as well as SMPSO as a particle swarm optimization, and compared their performance. As test case we optimize the parameters of the Grid ALU Processor (GAP) microarchitecture and then GAP together with the post-link code optimizer GAPtimize. An analysis of the simulation results shows a very good performance of all the three algorithms. SMPSO reveals the fastest convergence speed. A clear winner between NSGA-II and SPEA2 cannot be determined.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Calborean, H., Vintan, L.: An Automatic Design Space Exploration Framework for Multicore Architecture Optimizations. In: Proceedings of the 9th IEEE RoEduNet International Conference, pp. 202–207. IEEE Xplore Digital Library, Sibiu (2010)
Calborean, H., Jahr, R., Ungerer, T., Vintan, L.: Optimizing a Superscalar System using Multi-objective Design Space Exploration. In: Proceedings of the 18th International Conference on Control Systems and Computer Science (CSCS), Bucharest, Romania, pp. 339–346 (2011)
Calborean, H., Vintan, L.: Toward an efficient automatic design space exploration frame for multicore optimization. ACACES 2010 poster Abstracts, Terassa, Spain, pp. 135–138 (2010)
Calborean, H., Vinţan, L.: Framework for Automatic Design Space Exploration of Computer Systems. Acta Universitatis Cibiniensis Technical Series (2011)
Jahr, R., Ungerer, T., Calborean, H., Vintan, L.: Automatic Multi-Objective Optimization of Parameters for Hardware and Code Optimizations. In: Waleed, W., Smari, J.P.M. (eds.) Proceedings of the 2011 International Conference on High Performance Computing & Simulation (HPCS 2011), pp. 308–316. IEEE (2011)
Silvano, C., Fornaciari, W., Palermo, G., Zaccaria, V., Castro, F., Martinez, M., Bocchio, S., Zafalon, R., Avasare, P., Vanmeerbeeck, G., et al.: MULTICUBE: Multi-Objective Design Space Exploration of Multi-Core Architectures. In: Proceedings of the 2010 IEEE Annual Symposium on VLSI, pp. 488–493 (2010)
Palermo, G., Silvano, C., Zaccaria, V.: Discrete Particle Swarm Optimization for Multi-objective Design Space Exploration. Digital System Design Architectures, Methods and Tools. In: 11th EUROMICRO Conference on DSD 2008, pp. 641–644 (2008)
Lungu, V., Sofron, A.: Using Particle Swarm Optimization to Create Particle Systems. In: Proceedings of the 18th International Conference on Control Systems and Computer Science (CSCS-18), pp. 750–754. Politehnica Press, Bucharest (2011)
Kang, S., Kumar, R.: Magellan: a search and machine learning-based framework for fast multi-core design space exploration and optimization. In: Proceedings of the Conference on Design, Automation and Test in Europe, pp. 1432–1437. ACM, Munich (2008)
Jia, Z.J., Pimentel, A.D., Thompson, M., Bautista, T., Núnez, A.: Nasa: A generic infrastructure for system-level MP-SoC design space exploration. In: 8th IEEE Workshop on Embedded Systems for Real-Time Multimedia (ESTIMedia), pp. 41–50 (2010)
Nebro, A., Durillo, J., Garcıa-Nieto, J., Coello, C.A., Luna, F., Alba, E.: Smpso: A new pso-based metaheuristic for multi-objective optimization. In: Proceedings of the IEEE Symposium Series on Computational Intelligence, pp. 66–73 (2009)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm. Eurogen, pp. 95–100 (2001)
Desmet, V., Girbal, S., Temam, O., France, B.F.: Archexplorer. org: Joint compiler/hardware exploration for fair comparison of architectures. In: INTERACT workshop at HPCA 2009 (2009)
Coello, C.A.C., Veldhuizen, D.A.V., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer (2002)
Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Swiss Federal Institute of Technology, ETH (1999)
Sharkey, J.J., Ponomarev, D., Ghose, K.: M-SIM: A Flexible, Multithreaded Architectural Simulation Environment - Technical Report CS-TR-05-DP01 (2005)
Binkert, N.L., Dreslinski, R.G., Hsu, L.R., Lim, K.T., Saidi, A.G., Reinhardt, S.K.: The M5 Simulator: Modeling Networked Systems. IEEE Micro. 26, 52–60 (2006)
Radu, C., Vinţan, L.: Optimized Simulated Annealing for Network-on-Chip Application Mapping. In: Proceedings of the 18th International Conference on Control Systems and Computer Science (CSCS 2018), pp. 452–459. Politehnica Press, Bucharest (2011)
Ubal, R., Sahuquillo, J., Petit, S., López, P.: Multi2Sim: A Simulation Framework to Evaluate Multicore-Multithreaded Processors. In: Proc. of the 19th Int’l Symposium on Computer Architecture and High Performance Computing (2007)
Durillo, J.J., Nebro, A.J., Luna, F., Dorronsoro, B., Alba, E.: A Java Framework for Developing Multi-Objective Optimization Metaheuristics. Departamento de Lenguajes y Ciencias de la Computacion. University of Malaga, E.T.S.I. Informatica (2006)
Jahr, R., Calborean, H., Vintan, L., Ungerer, T.: Boosting Design Space Explorations with Existing or Automatically Learned Knowledge. In: Schmitt, J. (ed.) Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance, pp. 221–235. Springer, Heidelberg (2012)
Calborean, H.: Multi-Objective Optimization of Advanced Computer Architectures using Domain-Knowledge. PhD Thesis, “Lucian Blaga” University of Sibiu, Romania, 2011 (PhD Supervisor: Prof. Lucian Vintan, PhD) (2011)
Uhrig, S., Shehan, B., Jahr, R., Ungerer, T.: The Two-dimensional Superscalar GAP Processor Architecture. International Journal on Advances in Systems and Measurements 3, 71–81 (2010)
Jahr, R., Shehan, B., Uhrig, S., Ungerer, T.: Static Speculation as Post-Link Optimization for the Grid Alu Processor. In: Proceedings of the 4th Workshop on Highly Parallel Processing on a Chip, HPPC 2010 (2010)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. 6, 182–197 (2002)
Gellert, A., Calborean, H., Vintan, L., Florea, A.: Multi-Objective Optimizations for a Superscalar Architecture with Selective Value Prediction. IET Computers & Digital Techniques (accepted, manuscript ID CDT-2011-0116)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Calborean, H., Jahr, R., Ungerer, T., Vintan, L. (2013). A Comparison of Multi-objective Algorithms for the Automatic Design Space Exploration of a Superscalar System. In: Dumitrache, L. (eds) Advances in Intelligent Control Systems and Computer Science. Advances in Intelligent Systems and Computing, vol 187. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32548-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-32548-9_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32547-2
Online ISBN: 978-3-642-32548-9
eBook Packages: EngineeringEngineering (R0)