GPU Implementation of the Branch and Bound Method for Knapsack Problems

Lalami, Mohamed Esseghir; Baz, Didier El

doi:10.1109/ipdpsw.2012.219

Cited by 40 publications

(50 citation statements)

References 15 publications

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“…Reduction in computation time by a factor of 20 has been observed in [25] for strongly correlated problems with 500 variables. The reduction in computation time has been further improved by a factor of 52 in [26] for strongly correlated problems with 1,000 variables and some optimization in access to the GPU memory.…”

Section: ) Branch and Boundmentioning

confidence: 97%

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Recent Advances on GPU Computing in Operations Research

Boyer

Baz

2013

2013 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum

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Abstract-In the last decade, Graphics Processing Units (GPUs) have gained an increasing popularity as accelerators for High Performance Computing (HPC) applications. Recent GPUs are not only powerful graphics engines but also highly threaded parallel computing processors that can achieve sustainable speedup as compared with CPUs. In this context, researchers try to exploit the capability of this architecture to solve difficult problems in many domains in science and engineering. In this article, we present recent advances on GPU Computing in Operations Research. We focus in particular on Integer Programming and Linear Programming.

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Section: ) Branch and Boundmentioning

confidence: 97%

“…A hybrid Branch and Bound algorithm has been proposed in [25] for the solution of the knapsack problem. The GPU is used only when the number of Branch and Bound nodes is important.…”

Section: ) Branch and Boundmentioning

confidence: 99%

Recent Advances on GPU Computing in Operations Research

Boyer

Baz

2013

2013 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum

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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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“…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%

“…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. The authors proposed the parallelization of the branching and bounding operators.…”

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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GPU Implementation of the Branch and Bound Method for Knapsack Problems

Cited by 40 publications

References 15 publications

Recent Advances on GPU Computing in Operations Research

Recent Advances on GPU Computing in Operations Research

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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