Abstract
This paper describes a complete and efficient solution to the stochastic allocation and scheduling for Multi-Processor System-on-Chip (MPSoC). Given a conditional task graph characterizing a target application and a target architecture with alternative memory and computation resources, we compute an allocation and schedule minimizing the expected value of communication cost, being the communication resources one of the major bottlenecks in modern MPSoCs. Our approach is based on the Logic Based Benders decomposition where the stochastic allocation is solved through an Integer Programming solver, while the scheduling problem with conditional activities is faced with Constraint Programming. The two solvers interact through no-goods. The original contributions of the approach appear both in the allocation and in the scheduling part. For the first, we propose an exact analytic formulation of the stochastic objective function based on the task graph analysis, while for the scheduling part we extend the timetable constraint for conditional activities. Experimental results show the effectiveness of the approach.
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
Wu, D., Al-Hashimi, B., Eles, P.: Scheduling and mapping of conditional task graph for the synthesis of low power embedded systems. Computers and Digital Techniques, IEE Proceedings 150(5), 262–273 (2003)
Eles, P.P.P., Peng, Z.: Bus access optimization for distributed embedded systems based on schedulability analysis. In: International Conference on Design and Automation in Europe, DATE 2000. IEEE Computer Society Press, Los Alamitos (2000)
Shin, D., Kim, J.: Power-aware scheduling of conditional task graphs in real-time multiprocessor systems. In: International Symposium on Low Power Electronics and Design (ISLPED). ACM, New York (2003)
Wolf, W.: The future of multiprocessor systems-on-chips. In: Procs. of the 41st Design and Automation Conference - DAC 2004, San Diego, CA, USA, pp. 681–685. ACM, New York (2004)
Benini, L., Bertozzi, D., Guerri, A., Milano, M.: Allocation and scheduling for MPSOCs via decomposition and no-good generation. In: Proc. of the Intl. Conference in Principles and Practice of Constraint Programming (2005)
Hooker, J.N., Ottosson, G.: Logic-based benders decomposition. Mathematical Programming 96, 33–60 (2003)
Baptiste, P., Pape, C.L., Nuijten, W.: Constraint-Based Scheduling. Kluwer Academic Publishers, Dordrecht (2003)
Vilim, P., Bartak, R., Cepek, O.: Extension of o(n.log n) filtering algorithms for the unary resource constraint to optional activities. Constraints 10, 403–425 (2005)
Laborie, P.: Algorithms for propagating resource constraints in ai planning and scheduling: Existing approaches and new results. Journal of Artificial Intelligence 143, 151–188 (2003)
Kuchcinski, K.: Constraints-driven scheduling and resource assignment. ACM Transactions on Design Automation of Electronic Systems 8 (2003)
Ahmed, S., Shapiro, A.: The sample average approximation method for stochastic programs with integer recourse. In: Optimization on line (2002)
Laporte, G., Louveaux, F.V.: The integer l-shaped method for stochastic integer programs with complete recourse. Operations Research Letters 13 (1993)
Benders, J.F.: Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik 4, 238–252 (1962)
Norkin, V.I., Pflug, G., Ruszczynski, A.: A branch and bound method for stochastic global optimization. Mathematical Programming 83 (1998)
Walsh, T.: Stochastic constraint programming. In: Proc. of the European Conference on Artificial Intelligence, ECAI (2002)
Tarim, A., Manandhar, S., Walsh, T.: Stochastic constraint programming: A scenario-based approach. Constraints 11, 53–80 (2006)
Culler, D.A., Singh, J.P.: Parallel Computer Architecture: A Hardware/Software Approach. Morgan Kaufmann, San Francisco (1999)
Compton, K., Hauck, S.: Reconfigurable computing: A survey of systems and software. ACM Computing Surveys 34, 171–210 (1999)
Gomes, C.P., Selman, B., McAloon, K., Tretkoff, C.: Randomization in backtrack search: Exploiting heavy-tailed profiles for solving hard scheduling problems. In: AIPS, pp. 208–213 (1998)
Faraboschi, P., Fisher, J.A., Young, C.: Instruction scheduling for instruction level parallel processors. Proceedings of the IEEE 89(11), 1638–1659 (2001)
Lombardi, M., Milano, M.: Stochastic Allocation and Scheduling for Conditional Task Graphs in MPSoCs. Technical Report 77 LIA-003-06 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lombardi, M., Milano, M. (2006). Stochastic Allocation and Scheduling for Conditional Task Graphs in MPSoCs. In: Benhamou, F. (eds) Principles and Practice of Constraint Programming - CP 2006. CP 2006. Lecture Notes in Computer Science, vol 4204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11889205_23
Download citation
DOI: https://doi.org/10.1007/11889205_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46267-5
Online ISBN: 978-3-540-46268-2
eBook Packages: Computer ScienceComputer Science (R0)