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

Gmys, Jan; Mezmaz, Mohand; Melab, Nouredine; Tuyttens, Daniel

doi:10.1002/cpe.4019

Cited by 13 publications

(20 citation statements)

References 26 publications

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“…As these algorithms are compute-intensive, diverse strategies have been used for improving performance, such as instruction-level parallelism, architecturespecific code optimizations and problem-specific data structures [6,12,14,17]. Thus, parallel tree-based search algorithms are frequently written in C/C++, due to their low-level features and supported parallel computing libraries [5].…”

Section: Tree-based Search Algorithmsmentioning

confidence: 99%

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Productivity-Aware Design and Implementation of Distributed Tree-Based Search Algorithms

Carneiro

Melab

2019

Lecture Notes in Computer Science

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Parallel tree search algorithms offer viable solutions to problems in different areas, such as operations research, machine learning and artificial intelligence. This class of algorithms is highly computeintensive, irregular and usually relies on context-specific data structures and hand-made code optimizations. Therefore, C and C++ are the languages often employed, due to their low-level features and performance. In this work, we investigate the use of Chapel high-productivity language for the design and implementation of distributed tree search algorithms for solving combinatorial problems. The experimental results show that Chapel is a suitable language for this purpose, both in terms of performance and productivity. Despite the use of high-level features, the distributed tree search in Chapel is on average 16% slower and reaches up to 85% of the scalability observed for its MPI+OpenMP counterpart.

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Section: Tree-based Search Algorithmsmentioning

confidence: 99%

“…Tree-based search algorithms are compute-intensive and highly irregular, which demands hand-optimized data structures for efficient search and load balancing [6,14,17]. Thus, high-productivity languages are not often employed within the scope of tree search, as they historically suffer from severe performance penalties [11].…”

Section: Introductionmentioning

confidence: 99%

Productivity-Aware Design and Implementation of Distributed Tree-Based Search Algorithms

Carneiro

Melab

2019

Lecture Notes in Computer Science

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“…For instance, a performance comparison is available in [12] between multi-core and many-core systems by solving big optimization problems with a Branch-andBound algorithm. Another branch-and-bound implementation is described in [8] using multi-GPU systems. While the previous papers are related to multi-CPU systems on one hand and to multi-GPU systems on another hand, [13] implements a Branch-and-Bound for heterogeneous architectures (both multi-CPU systems with GPU accelerators).…”

Section: Multithreaded Branch-and-boundmentioning

confidence: 99%

“…[6]), and several authors investigated sophisticated approaches to take advantage of various architectures (e.g. [7], [8]). Other authors proposed approaches to provide a more general framework to design such parallel Branch-and-Bound algorithms (e.g.…”

mentioning

confidence: 99%

Solving 0-1 Quadratic Problems with Two-Level Parallelization of the BiqCrunch Solver

Coti

Leclercq

Roupin

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-In this paper we present MLTBiqCrunch, a hierarchically parallelized version of the open-source solver BiqCrunch [1]. More precisely, this version has two levels of parallelization: a coarse grain, assigning a thread to a node evaluation and a fine grain, parallelizing a node evaluation when some threads are not busy. We present experiments on some classical binary quadratic optimization problems with comparison of their scalability and raw performance. In particular, we obtain a superlinear speedup for some of the most difficult instances.

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“…Paper [4] presents a novel B&B algorithm for solving large permutation-based combinatorial optimization problems. A hierarchical work stealing strategy was proposed, in order to tackle the problems of the irregular workload, as well as to balance the workload inside the GPU and between different GPUs and CPU cores.…”

mentioning

confidence: 99%

Algorithmic advances for parallel architectures

Wyrzykowski

Szymański

2017

Concurrency and Computation

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This special issue of Concurrency and Computation: Practice and Experience contains revised and extended versions of selected papers presented at the 11th International Conference on Parallel Processing and Applied Mathematics, PPAM 2015, which was held on September 6 to 9, 2015, in Krakow, Poland.PPAM is a biennial series of international conferences dedicated to exchanging ideas between researchers involved in parallel and distributed computing, including theory and applications, as well as applied and computational mathematics. The focus of PPAM 2015 was on models, algorithms, and software tools that facilitate efficient and convenient use of modern parallel and distributed computing systems, as well as on large-scale applications, including data-intensive problems. This meeting gathered more than 190 participants from 33 countries. A strict reviewing process, with each submission reviewed at least three times, resulted in acceptance of 111 contributed papers with the acceptance rate of 57%. The accepted papers were presented at the regular tracks of the PPAM 2015 conference, as well as during the workshops that were important and integral parts of the PPAM 2015 meeting.Based on the results of the reviews, selected papers were recommended for a special journal issue. Besides quality, another important goal that influenced the paper selection was a maximum possible thematic consistency of the issue. The focus of this special issue is on algorithmic advances to better match the software properties to the targeted parallel architecture. These advances vary from general, like increasing potential reuse of a part of computation, to specific for a particular architecture, eg, using GPUs or with multi-core and many-core processors, or specific applications, such as spherical Delaunay triangulations, or visualization of complex networks. The authors were contacted after the conference and invited to submit revised and extended versions of their papers. These new versions were reviewed independently by three reviewers. Finally, nine contributions were accepted for publication. They are summarized below.Paper [1] focuses on LU factorization, which is a recurrent operation in scientific and engineering applications used to solve linear systems. In particular, the authors adapt the Gauss-Huard algorithm that has been introduced as an efficient solution for modern platforms equipped with accelerators to improve reusing of computations in this algorithm. This approach was evaluated on the solution of Lyapunov matrix equations via the LRCF-ADI method and validated on three benchmarks. In future work, the authors plan to design a heuristic for choosing the optimal block size and to integrate their solution with the mixed precision techniques to further accelerate the Lyapunov solver.An efficient algorithm for solving dense symmetric indefinite systems on GPUs is presented in the work of Baboulin et al. [2]. The critical challenge on these types of computations on hybrid CPU/GPU is to keep to minimum the expensive data transf...

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

Cited by 13 publications

References 26 publications

Productivity-Aware Design and Implementation of Distributed Tree-Based Search Algorithms

Productivity-Aware Design and Implementation of Distributed Tree-Based Search Algorithms

Solving 0-1 Quadratic Problems with Two-Level Parallelization of the BiqCrunch Solver

Algorithmic advances for parallel architectures

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