A novel cooperative accelerated parallel two-list algorithm for solving the subset-sum problem on a hybrid CPU-GPU cluster

TL;DR: BalsubLast is an improved balanced algorithm for the Subset-Sum Problem that was designed to solve benchmarks that require the exhaustion of the search space and where there are many subsets with the same sum.

TL;DR: In this paper, a new scheme to sample solutions for the subset sum problem based on swarm-based optimization algorithms with distinct forms of selection pressure, the balance of exploration-exploitation, the multimodality considerations, and a search space defined by numbers associated with subsets of fixed size.

TL;DR: In this article, a CPU-GPU cooperative implementation of a parallel two-list algorithm is proposed to solve the subset-sum problem in a heterogeneous CPU and GPU system, which enables the efficient utilization of all the available computational resources of both CPUs and GPUs.

TL;DR: In this paper, the authors address the subset sum problem by using gradient-free optimization with a number-based representation of the combinatorial search space and by generating combinations with minimal change order through parallel reductions in the GPU.

TL;DR: A technique is developed which improves all of the dynamic programming methods by asquare root factor and can be incorporated into the more general 0-1 knapsack problem obtaining a square root improvement in the asymptotic behavior.

TL;DR: The need for new algorithms that would split the computation in a way that would fully exploit the power that each of the hybrid components possesses is motivated, and the need for a DLA library similar to LAPACK but for hybrid manycore/GPU systems is envisioned.

TL;DR: In this article, the authors highlight the trends leading to the idea of hybrid manycore/GPU systems, and present a set of techniques that can be used to eciently program them.

TL;DR: It is shown that the DSKP has an optimal policy that consists of an easily computed threshold acceptance rule and an optimal stopping rule that maximizes the expected value (rewards minus costs) accumulated.