Graphics processing unit‐accelerated bounding for branch‐and‐bound applied to a permutation problem using data access optimization

Melab, Nouredine; Chakroun, Imen; Bendjoudi, Ahcène

doi:10.1002/cpe.3155

Cited by 13 publications

(29 citation statements)

References 14 publications

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“…They are composed of two or more independent central processing units (cores) that can run multiple instructions at the same time. The use of these processors allow developers to gain in performance by parallelizing their applications exploiting simultaneously the cores [5,33]. However, when launching a traditional sequential application on multi-core CPU, only one core is exploited if the application is not explicitly parallelized to run on the different cores.…”

Section: Solution Quality Performancementioning

confidence: 99%

GPU-based bees swarm optimization for association rules mining

et al. 2015

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Association rules mining (ARM) is a well-known combinatorial optimization problem aiming at extracting relevant rules from given large-scale datasets. According to the state of the art, the bio-inspired methods proved their efficiency by generating acceptable solutions in a reasonable time when dealing with small and medium size instances. Unfortunately, to cope with large instances such as the webdocs benchmark, these methods require more and more powerful processors and are time expensive. Nowadays, computing power is no longer a real issue. It can be provided by the power of emerging technologies such as graphics processing units (GPUs) that are massively multi-threaded processors. In this paper, we investigate the use of GPUs to speed up the computation. We propose two GPU-based bees swarm algorithms for association rules mining (single evaluation in GPU, SE-GPU and multiple evaluation in GPU, ME-GPU). SE-GPU aims at evaluating one rule at a time where each thread is associated with one transaction, whereas ME-GPU evaluates multiple rules in parallel on GPU where each thread is associated with several transactions. To validate our 123 Y. Djenouri et al. approaches, the two algorithms have been executed to solve well-known large ARM instances. Real experiments have been carried out on an Intel Xeon 64 bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that our approaches improve the execution time up to 100× over the sequential mono-core bees swarm optimization-ARM algorithm. Moreover, the proposed approaches have been compared with CPU multi-core ones (1-8 cores). The results show that they are faster than the multi-core versions whatever is the number of used cores.

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Section: Solution Quality Performancementioning

confidence: 99%

GPU-based bees swarm optimization for association rules mining

et al. 2015

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“…For large values of the parameters m and n, the problem is time-intensive. More details on the lower bound and its computational complexity can be found in [13].…”

Section: The Flow-shop Scheduling Problem (Fsp)mentioning

confidence: 99%

“…Such result demonstrates that the bounding operator needs massively parallel computing. The GPU-accelerated bounding has been investigated in [13]. The reported results show that the CPU-GPU data transfer is costly.…”

Section: Introductionmentioning

confidence: 99%

Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors

Melab

Gmys

Mezmaz

et al. 2019

High-Performance Simulation-Based Optimization

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“…The GPUaccelerated B&B-algorithms proposed in [4,19] are based on the PEB model, offloading pools of subproblems for bounding to the GPU. The degree of parallelism in this model depends on the problem size and the depth of the evaluated node in the tree.…”

Section: Models For Parallel Branch-and-boundmentioning

confidence: 99%

IVM‐based parallel branch‐and‐bound using hierarchical work stealing on multi‐GPU systems

Gmys

Mezmaz

Melab

et al. 2016

Concurrency and Computation

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International audienceTree-based exploratory methods, like Branch-and-Bound (B&B) algorithms, are highly irregular applications which makes their design and implementation on graphics processing unit (GPU) challenging. In this paper, we present a multi-GPU B&B algorithm for solving large permutation-based combinatorial optimization problems. To tackle the problem of the irregular workload, we propose a hierarchical work stealing (WS) strategy that balances the workload inside the GPU and between different GPUs and CPU cores. Our B&B is based on an Integer-Vector-Matrix data structure instead of a pool of permutations, and work units exchanged are intervals of factoradics instead of sets of nodes. Two variants of the algorithm, using the same hierarchical WS strategy, are proposed: one for combinatorial optimization problems where the evaluation of nodes is costly and one for fine-grained problems. The latter variant uses a new hypercube-based WS strategy and a trigger mechanism to balance the work load inside the GPU. The proposed approach has been extensively experimented using the flowshop scheduling, the n-queens and the asymmetric travelling salesman problems as test-cases. The reported results show that the proposed hierarchical WS mechanism is capable of handling fine and coarse-grained types of workloads efficiently, reaching near-linear speed-up on up to four GPUs for a set of ten flowshop instances and large instances of fine-grained problem

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Graphics processing unit‐accelerated bounding for branch‐and‐bound applied to a permutation problem using data access optimization

Cited by 13 publications

References 14 publications

GPU-based bees swarm optimization for association rules mining

GPU-based bees swarm optimization for association rules mining

Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors

IVM‐based parallel branch‐and‐bound using hierarchical work stealing on multi‐GPU systems

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