Core Problems in Knapsack Algorithms
Operations Research
Code-Size Minimization in Multiprocessor Real-Time Systems
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 2 - Volume 03
Instruction-set customization for real-time embedded systems
Proceedings of the conference on Design, automation and test in Europe
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VLSID '09 Proceedings of the 2009 22nd International Conference on VLSI Design
Hard multidimensional multiple choice knapsack problems, an empirical study
Computers and Operations Research
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ICPADS '09 Proceedings of the 2009 15th International Conference on Parallel and Distributed Systems
Solving knapsack problems on GPU
Computers and Operations Research
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Variants of the 0-1 knapsack problem manifest themselves at the core of several system-level optimization problems. The running times of such system-level optimization techniques are adversely affected because the knapsack problem is NP-hard. In this paper, we propose a new GPU-based approach to accelerate the multiple-choice knapsack problem, which is a general version of the 0-1 knapsack problem. Apart from exploiting the parallelism offered by the GPUs, we also employ a variety of GPU-specific optimizations to further accelerate the running times of the knapsack problem. Moreover, our technique is scalable in the sense that even when running large instances of the multiple-choice knapsack problems, we can efficiently utilize the GPU compute resources and memory bandwidth to achieve significant speedups.