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• The adaptive bulk search is a QUBO solver alternative to quantum annealing. • The cyclic-min algorithm is a SA-like algorithm without random number generation. • The bit permutated cyclic-min algorithm provides near-optimal solutions for QUBO. • Our scalable implementation attains linear improvement of the search rate. A wide range of combinatorial optimization problems can be reduced to the Ising model, and equivalently the quadratic unconstrained binary optimization (QUBO) problem. Thus, in recent years, researchers have proposed to solve QUBO on FPGAs, GPUs, and special-purpose processors. The adaptive bulk search (ABS) is a previously-proposed framework for solving QUBO in parallel on multiple GPUs. In the ABS, a CPU host performs a GA-based global search while GPUs asynchronously perform many local searches in parallel. An original ABS adopts a simple local search algorithm called a cyclic-min algorithm, which does not use pseudo random numbers. However, the lack of randomness may cause a potential drawback of restricted bit-flipping operations in a local search. To avoid this drawback, this paper proposes a cyclic-min algorithm with randomly-generated multiple bit permutations, which enables a more effective local search with random number generation in CPUs (not in GPUs). Furthermore, this paper introduces a scalable implementation of the ABS with MPI and OpenMP. Our experimental results on TSUBAME3.0 show that the solution quality improves and the throughput linearly increases as the number of GPUs increases; with 256 GPUs, it evaluates 20.1 × 10 12 solutions per second. [ABSTRACT FROM AUTHOR]

• Formulate a non-cooperative game with both UEs and MECs as players. • Prove the existence of and convergence to a Nash equilibrium. • Develop a set of algorithms to find the Nash equilibrium. • Demonstrate numerical examples of non-cooperative games with and without MECs' participation. In a newly emerged fog computing environment, various user equipments (UE) enhance their computing power and extend their battery lifetime by computation offloading to mobile edge cloud (MEC) servers. Such an environment is distributed and competitive in nature. In this paper, we take a game theoretical approach to computation offloading optimization in a fog computing environment. Such an approach captures and characterizes the nature of a competitive environment. The main contributions of the paper can be summarized as follows. First, we formulate a non-cooperative game with both UEs and MECs as players. Each UE attempts to minimize the execution time of its tasks with an energy constraint. Each MEC attempts to minimize the product of its power consumption for computation and execution time for allocated tasks. Second, we develop a heuristic algorithm for a UE to determine its "heuristically" best response to the current situation, an algorithm for an MEC to determine its best response to the current situation, and an iterative algorithm to find the Nash equilibrium. Third, we prove that our iterative algorithm converges to a Nash equilibrium. We demonstrate numerical examples of our non-cooperative games with and without MECs' participation. We observe that our iterative algorithm always quickly converges to a Nash equilibrium. The uniqueness of our non-cooperative games is that the strategy set of a player can be discrete and the payoff function of a player can be obtained by a heuristic algorithm for combinatorial optimization. To the best of the author's knowledge, there has been no such investigation of non-cooperative games based on combinatorial optimization for computation offloading optimization in a fog computing environment. [ABSTRACT FROM AUTHOR]

In this paper, we describe the first parallel Branch-and-Bound (B&B) algorithm with a history-based domination technique. Although history-based domination substantially speeds up a B&B search, it makes parallelization much more challenging. Our algorithm is the first parallel exact algorithm for the Sequential Ordering Problem using a pure B&B approach. To effectively explore the solution space, we have developed three novel parallelization techniques: thread restart, parallel history domination, and history-table memory management. The proposed algorithm was experimentally evaluated using the SOPLIB and TSPLIB benchmarks on multi-core processors. Using 32 threads with a time limit of one hour, the algorithm gives geometric-mean speedups of 72x and 20x on the medium-difficulty SOPLIB and TSPLIB instances, respectively. On the hard instances, it solves 12 instances that the sequential algorithm does not solve, with geometric-mean speedups of 16x on SOPLIB and 32x on TSPLIB. Super-linear speedups up to 366x are seen on 16 instances. • We propose the first parallel B&B algorithm that involves a history-based domination technique. • The proposed algorithm includes three novel parallelization techniques: thread restart, parallel history-based domination, and history-table memory management. • The proposed algorithm is the first parallel B&B algorithm for the Sequential Ordering Problem, which is an NP-hard sequencing problem with precedence constraints. • We present a thorough experimental evaluation of the proposed parallel algorithm on SOPLIB and TSPLIB. The results show that super-linear speedup is not an anomaly; it can be achieved on many instances. We report a speedup ratio as high as 285 on a 32-core processor. [ABSTRACT FROM AUTHOR]

Abstract: Estimation of distribution algorithms (EDAs) are a wide-ranging family of evolutionary algorithms whose common feature is the way they evolve by learning a probability distribution from the best individuals in a population and sampling it to generate the next one. Although they have been widely applied to solve combinatorial optimization problems, there are also extensions that work with continuous variables. In this paper [this paper is an extended version of delaOssa et al. Initial approaches to the application of islands-based parellel EDAs in continuous domains, in: Proceedings of the 34th International Conference on Parallel Processing Workshops (ICPP 2005 Workshops), Oslo, 2005, pp. 580–587] we focus on the solution of the latter by means of island models. Besides evaluating the performance of traditional island models when applied to EDAs, our main goal consists in achieving some insight about the behavior and benefits of the migration of probability models that this framework allow. [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of task allocation in heterogeneous distributed systems with the goal of maximizing the system reliability. It first develops an allocation model for reliability based on a cost function representing the unreliability caused by the execution of tasks on the system processors and the unreliability caused by the interprocessor communication time subject to constraints imposed by both the application and the system resources. It then presents a heuristic algorithm derived from the well-known simulated annealing (SA) technique to quickly solve the mentioned problem. The performance of the proposed algorithm is evaluated through experimental studies on a large number of randomly generated instances. Indeed, the quality of solutions are compared with those derived by using the branch-and-bound (BB) technique. [Copyright &y& Elsevier]

Abstract: The Problem of obtaining an optimal matching and scheduling of interdependent tasks in distributed heterogeneous computing (DHC) environments is well known to be an NP-hard problem. In a DHC system, task execution time is dependent on the machine to which it is assigned and task precedence constraints are represented by a directed acyclic graph. Recent research in evolutionary techniques has shown that genetic algorithms usually obtain more efficient schedules that other known algorithms. We propose a non-evolutionary random scheduling (RS) algorithm for efficient matching and scheduling of inter-dependent tasks in a DHC system. RS is a succession of randomized task orderings and a heuristic mapping from task order to schedule. Randomized task ordering is effectively a topological sort where the outcome may be any possible task order for which the task precedent constraints are maintained. A detailed comparison to existing evolutionary techniques (GA and PSGA) shows the proposed algorithm is less complex than evolutionary techniques, computes schedules in less time, requires less memory and fewer tuning parameters. Simulation results show that the average schedules produced by RS are approximately as efficient as PSGA schedules for all cases studied and clearly more efficient than PSGA for certain cases. The standard formulation for the scheduling problem addressed in this paper is ., [Copyright &y& Elsevier]