Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems
INFORMS Journal on Computing
QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Allocation and scheduling for MPSoCs via decomposition and no-good generation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Stochastic allocation and scheduling for conditional task graphs in MPSoCs
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
CSCLP'05 Proceedings of the 2005 Joint ERCIM/CoLogNET international conference on Constraint Solving and Constraint Logic Programming
Allocation, scheduling and voltage scaling on energy aware MPSoCs
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
A Constraint Programming Approach for Allocation and Scheduling on the CELL Broadband Engine
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
A 25-year perspective on logic programming
INFORMS Journal on Computing
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Software optimization for multicore architectures is one of the most critical challenges in today's high-end computing. In this paper we focus on a well-known multicore platform, namely the Cell BE processor, and we address the problem of allocating and scheduling its processors, communication channels and memories, with the goal of minimizing application execution time. We have developed a complete optimization strategy based on Benders' decomposition. Unfortunately, a traditional two-stage decomposition produces unbalanced components: the allocation part is difficult, while the scheduling part is much easier. To address this issue, we have developed a multi-stage decomposition, which is a recursive application of standard Logic based Benders' Decomposition (LBD). Our experiments demonstrate that this approach is very effective in obtaining balanced sub-problems and in reducing the runtime of the optimizer.