A New Parallel Schema for Branch-and-Bound Algorithms Using GPGPU

Carneiro, Tiago; Muritiba, Albert Einstein Fernandes; Negreiros, Marcos; Campos, Gustavo Augusto Lima de

doi:10.1109/sbac-pad.2011.20

Cited by 28 publications

(31 citation statements)

References 30 publications

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“…However, because of the irregularity of the B&B tree search, scheduling the load inside a GPU device is not fully compatible with the underlying SIMD programming model. In [10,16,39,7,34,11], implementations on a single GPU device are presented with respect to specific optimization problems, e.g., Flowshop, Knapsack and TSP (Traveling salesman). With respect to this paper, these studies are rather of limited interest since they are problem specific and do not consider a heterogeneous and large scale setting.…”

Section: Parallel Bandb With Gpusmentioning

confidence: 99%

Parallel Branch-and-Bound in multi-core multi-CPU multi-GPU heterogeneous environments

Derbel

2016

Future Generation Computer Systems

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We investigate the design of parallel B&B in large scale heterogeneous compute environments where processing units can be composed of a mixture of multiple shared memory cores, multiple distributed CPUs and multiple GPUs devices. We describe two approaches addressing the critical issue of how to map B&B workload with the different levels of parallelism exposed by the target compute platform. We also contribute a throughout large scale experimental study which allows us to derive a comprehensive and fair analysis of the proposed approaches under different system configurations using up to 16 GPUs and up to 512 distributed cores. Our results shed more light on the main challenges one has to face when tackling B&B algorithms while describing efficient techniques to address them. In particular, we are able to obtain linear speed-ups at moderate scales where adaptive load balancing among the heterogeneous compute resources is shown to have a significant impact on performance. At the largest scales, intranode parallelism and hybrid decentralized load balancing is shown to have a crucial importance in order to alleviate locking issues among shared memory threads and to scale the distributed resources while optimizing communication costs and minimizing idle times.

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Section: Parallel Bandb With Gpusmentioning

confidence: 99%

Parallel Branch-and-Bound in multi-core multi-CPU multi-GPU heterogeneous environments

Derbel

2016

Future Generation Computer Systems

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“…Many contributions have proposed the design of parallel B&B methods on top of different architectures, using Massively Parallel Processors in [1], Networks or Clusters of Workstations in [14] and Shared Memory machines in [5]. However, few insights [9,4] into designing B&B on GPUs exist. In [9], B&B has been applied to the knapsack problem which is solved using a binary search tree.…”

Section: Branch-and-bound Algorithmmentioning

confidence: 99%

“…The authors proposed the parallelization of the branching and bounding operators. In [4], B&B is applied to the traveling salesman problem. They proposed to divide the global search space into disjoint sub-spaces that are explored in parallel by the GPU threads.…”

Section: Branch-and-bound Algorithmmentioning

confidence: 99%

Towards a heterogeneous and adaptive parallel Branch-and-Bound algorithm

Chakroun

Melab

2015

Journal of Computer and System Sciences

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International audienceIn this work, we revisit the design and implementation of the Branch-and-Bound (B&B) algorithm for heterogeneous environments combining multi-core processors with GPU accelerators. The challenge is to automatically identify the best mapping of computations to the target heterogeneous platform and to adjust the problem, algorithmic and platform parameters. In the proposed meta-algorithm, four hardware configuration scenarios have been considered: 1CPU-1GPU, nCPU-0GPU, nCPU-1GPU, nCPU-nGPU.Over a serial B&B, accelerations up to ×222 have been achieved for large problem instances of the Flow-Shop scheduling problem. The reported results show that: (1) the GPU-accelerated algorithm performs better than its multi-core CPU-based version; (2) combining multi-core and GPU allows improvement up to 36% over a single CPU-GPU execution; (3) the more GPU devices are used, the better the speedups are whatever is the considered problem instance

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“…The parallelization strategy adopted by Carneiro et al (2011) is called Collegial B&B. In this strategy, several independent search procedures are performed in parallel.…”

Section: Case Study: Complete Enumeration In Atspmentioning

confidence: 99%

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2014

Lecture Notes in Computer Science

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A New Parallel Schema for Branch-and-Bound Algorithms Using GPGPU

Cited by 28 publications

References 30 publications

Parallel Branch-and-Bound in multi-core multi-CPU multi-GPU heterogeneous environments

Parallel Branch-and-Bound in multi-core multi-CPU multi-GPU heterogeneous environments

Towards a heterogeneous and adaptive parallel Branch-and-Bound algorithm

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