Divisible loads are parallel applications with fine granularity and negligible data dependencies. Such computations can be divided into parts of arbitrary sizes and processed independently in parallel. The load distri...
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Divisible loads are parallel applications with fine granularity and negligible data dependencies. Such computations can be divided into parts of arbitrary sizes and processed independently in parallel. The load distribution process incurs considerable communication delays. To reduce processor waiting time during the computation initialization phase, the load is distributed in multiple small installments rather than in one big chunk. In this paper we analyze multi-installment divisible load processing in heterogeneous distributed systems. Scheduling divisible loads in heterogeneous systems is hard because the sizes of the installments should be adjusted to the communication and computation capabilities of the system. We show that ignoring heterogeneity of the distributed system may result in arbitrarily bad solutions. Two algorithms are proposed to gear the load chunk sizes to different communication and computation speeds: an optimization branch-and-bound algorithm and a heuristic based on a genetic search method. The running times of both methods and the quality of the solutions are compared. Then, we use these algorithms to study the features of the multi-installment divisible load scheduling problem. We demonstrate that it has both combinatorial and algebraic nature, and that optimum solutions are harder to find with the growing heterogeneity of the system. Copyright (c) 2007 John Wiley & Sons, Ltd.
The problem of social workers visiting their patients at home is a class of combinatorialoptimization problems and belongs to the class of problems known as NP-Hard. These problems require heuristic techniques to pro...
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The problem of social workers visiting their patients at home is a class of combinatorialoptimization problems and belongs to the class of problems known as NP-Hard. These problems require heuristic techniques to provide an efficient solution in the best of cases. In this article, in addition to providing a detailed resolution of the social workers’ problem using the Quadratic Unconstrained Binary optimization Problems (QUBO) formulation, an approach to mapping the inequality constraints in the QUBO form is given. Finally, we map it in the Hamiltonian of the Ising model to solve it with the Quantum Exact Solver and Variational Quantum Eigensolvers (VQE). The quantum feasibility of the algorithm will be tested on IBMQ computers.
Recently, there is growing attention on applying deep reinforcement learning (DRL) to solve the 3D bin packing problem (3D BPP), given its favorable generalization and independence of ground-truth label. However, due ...
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ISBN:
(纸本)9781713832621
Recently, there is growing attention on applying deep reinforcement learning (DRL) to solve the 3D bin packing problem (3D BPP), given its favorable generalization and independence of ground-truth label. However, due to the relatively less informative yet computationally heavy encoder, and considerably large action space inherent to the 3D BPP, existing methods are only able to handle up to 50 boxes. In this paper, we propose to alleviate this issue via an end-to-end multimodal DRL agent, which sequentially addresses three sub-tasks of sequence, orientation and position, respectively. The resulting architecture enables the agent to solve large-scale instances of 100 boxes or more. Experiments show that the agent could learn highly efficient policies that deliver superior performance against all the baselines on instances of various scales.
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