scholarly journals A Multi-Branch-and-Bound Binary Parallel Algorithm to Solve the Knapsack Problem 0–1 in a Multicore Cluster

2019 ◽  
Vol 9 (24) ◽  
pp. 5368 ◽  
Author(s):  
José Crispín Zavala-Díaz ◽  
Marco Antonio Cruz-Chávez ◽  
Jacqueline López-Calderón ◽  
José Alberto Hernández-Aguilar ◽  
Martha Elena Luna-Ortíz
Keyword(s):  
Exact Solution ◽  
Parallel Algorithms ◽  
Parallel Algorithm ◽  
Branch And Bound ◽  
Knapsack Problem ◽  
Multicore Processors ◽  
Sequential Algorithm ◽  
Branch And Bound Algorithms ◽  
Superlinear Speedup

This paper presents a process that is based on sets of parts, where elements are fixed and removed to form different binary branch-and-bound (BB) trees, which in turn are used to build a parallel algorithm called “multi-BB”. These sequential and parallel algorithms calculate the exact solution for the 0–1 knapsack problem. The sequential algorithm solves the instances published by other researchers (and the proposals by Pisinger) to solve the not-so-complex (uncorrelated) class and some problems of the medium-complex (weakly correlated) class. The parallel algorithm solves the problems that cannot be solved with the sequential algorithm of the weakly correlated class in a cluster of multicore processors. The multi-branch-and-bound algorithms obtained parallel efficiencies of approximately 75%, but in some cases, it was possible to obtain a superlinear speedup.

scholarly journals SEQUENTIAL AND PARALLEL ALGORITHMS FOR CAUSAL EXPLANATION WITH BACKGROUND KNOWLEDGE

2004 ◽  
Vol 12 (supp02) ◽  
pp. 101-122
Author(s):  
BHASKARA REDDY MOOLE ◽  
MARCO VALTORTA
Keyword(s):  
Parallel Algorithms ◽  
Parallel Algorithm ◽  
Causal Explanation ◽  
Background Knowledge ◽  
Sequential Algorithm ◽  
Dependency Model

This paper presents a new sequential algorithm to answer the question about the existence of a causal explanation for a set of independence statements (a dependency model), which is consistent with a given set of background knowledge. Emphasis is placed on generality, efficiency and ease of parallelization of the algorithm. From this sequential algorithm, an efficient, scalable, and easy to implement parallel algorithm with very little inter-processor communication is derived.

scholarly journals Area Optimization of Slicing Floorplans in Parallel

VLSI Design ◽  
1994 ◽  
Vol 2 (2) ◽  
pp. 143-156
Author(s):  
Cheng-Hsi Chen ◽  
Ioannis G. Tollis
Keyword(s):  
Parallel Algorithms ◽  
Parallel Algorithm ◽  
Shared Memory ◽  
Distributed System ◽  
Sequential Algorithm ◽  
Area Optimization

We first present a parallel algorithm for finding the optimal implementations for the modules of a slicing floorplan that respects a given slicing tree. The algorithm runs in O(n) time and requires O(n) processors, where n is the number of modules. It is based on a new O(n2) sequential algorithm for solving the above problem. We then present a parallel algorithm for finding a set of optimal implementations for a slicing floorplan whose corresponding slicing tree has height O(logn). This algorithm runs in O(n) time using O(logn) processors. Our parallel algorithms do not need shared memory and can be implemented in a distributed system.

Analysis of a Step-Based Watershed Algorithm Using CUDA

2010 ◽  
Vol 1 (4) ◽  
pp. 16-28 ◽  
Author(s):  
Giovani Bernardes Vitor ◽  
André Körbes ◽  
Roberto de Alencar Lotufo ◽  
Janito Vaqueiro Ferreira
Keyword(s):  
Parallel Algorithms ◽  
Parallel Algorithm ◽  
Execution Time ◽  
Sequential Algorithm ◽  
Graphics Hardware ◽  
Watershed Algorithm ◽  
Sequential Approach ◽  
Watershed Transform ◽  
Average Speed ◽  
Hybrid Approaches

This paper proposes and develops a parallel algorithm for the watershed transform, with application on graphics hardware. The existing proposals are discussed and its aspects briefly analysed. The algorithm is proposed as a procedure of four steps, where each step performs a task using different approaches inspired by existing techniques. The algorithm is implemented using the CUDA libraries and its performance is measured on the GPU and compared to a sequential algorithm running on the CPU, achieving an average speed of twice the execution time of the sequential approach. This work improves on previous results of hybrid approaches and parallel algorithms with many steps of synchronisation and iterations between CPU and GPU.

Analysis of a Step-Based Watershed Algorithm Using CUDA

2012 ◽  
pp. 321-335 ◽  
Author(s):  
Giovani Bernardes Vitor ◽  
André Körbes ◽  
Roberto de Alencar Lotufo ◽  
Janito Vaqueiro Ferreira
Keyword(s):  
Parallel Algorithms ◽  
Parallel Algorithm ◽  
Execution Time ◽  
Sequential Algorithm ◽  
Graphics Hardware ◽  
Watershed Algorithm ◽  
Sequential Approach ◽  
Watershed Transform ◽  
Average Speed ◽  
Hybrid Approaches

This paper proposes and develops a parallel algorithm for the watershed transform, with application on graphics hardware. The existing proposals are discussed and its aspects briefly analysed. The algorithm is proposed as a procedure of four steps, where each step performs a task using different approaches inspired by existing techniques. The algorithm is implemented using the CUDA libraries and its performance is measured on the GPU and compared to a sequential algorithm running on the CPU, achieving an average speed of twice the execution time of the sequential approach. This work improves on previous results of hybrid approaches and parallel algorithms with many steps of synchronisation and iterations between CPU and GPU.

scholarly journals Branch-and-bound algorithms: A survey of recent advances in searching, branching, and pruning

2016 ◽  
Vol 19 ◽  
pp. 79-102 ◽  
Author(s):  
David R. Morrison ◽  
Sheldon H. Jacobson ◽  
Jason J. Sauppe ◽  
Edward C. Sewell
Keyword(s):  
Branch And Bound ◽  
Branch And Bound Algorithms ◽  
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