High-throughput Ant Colony Optimization on graphics processing units

Cecilia, José M.; Llanes, Antonio; Abellán, José Luis; Gómez-Luna, Juan; Chang, L.; Hwu, Wen-mei W.

doi:10.1016/j.jpdc.2017.12.002

Cited by 25 publications

(25 citation statements)

References 18 publications

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“…In the MMAS, a number of ants (agents) iteratively construct solutions to a combinatorial optimization problem (COP) [43]. In this paper, we focus on the TSP, following the existing research on parallel ACO [3,11,12,14,51,8], although, in principle, the ACO algorithms can be applied to any COP [15].…”

Section: Max-min Ant Systemmentioning

confidence: 99%

“…In recent work, Cecilia et al [8] discussed several aspects of an efficient GPU-based AS implementation. Specifically, they introduced a parallel implementation of the RWM that uses scan and stencil patterns to efficiently select an unvisited node.…”

Section: Recent Advancementsmentioning

confidence: 99%

“…Only recently has Cecilia et. al [8] proposed the parallel SS-Roulette method, which is essentially a parallel version of the RWM, i.e., it offers the same quality of results as the sequential implementation. This was possible mainly due to the increasing computational capacity of GPU architectures, and also improvements on the software side, e.g., the CUDA toolkit.…”

Section: Parallel Rwmmentioning

confidence: 99%

“…• The computational evaluation based on a set of TSP instances ranging from 198 to 3,795 cities shows that the proposed GPU-based MMAS is competitive with state-of-the-art GPU-based [11,12,8] and multi-core CPU-based [53] parallel ACO implementations. The times obtained were up to 7.18x and 21.79x smaller, respectively.…”

Section: Introductionmentioning

confidence: 98%

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Implementing a GPU-based parallel MAX–MIN Ant System

Skinderowicz

2020

Future Generation Computer Systems

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The MAX-MIN Ant System (MMAS) is one of the best-known Ant Colony Optimization (ACO) algorithms proven to be efficient at finding satisfactory solutions to many difficult combinatorial optimization problems. The slow-down in Moore's law, and the availability of graphics processing units (GPUs) capable of conducting general-purpose computations at high speed, has sparked considerable research efforts into the development of GPU-based ACO implementations. In this paper, we discuss a range of novel ideas for improving the GPU-based parallel MMAS implementation, allowing it to better utilize the computing power offered by two subsequent Nvidia GPU architectures. Specifically, based on the weighted reservoir sampling algorithm we propose a novel parallel implementation of the node selection procedure, which is at the heart of the MMAS and other ACO algorithms. We also present a memory-efficient implementation of another keycomponent -the tabu list structure -which is used in the ACO's solution construction stage. The proposed implementations, combined with the existing approaches, lead to a total of six MMAS variants, which are evaluated on a set of Traveling Salesman Problem (TSP) instances ranging from 198 to 3,795 cities. The results show that our MMAS implementation is competitive with state-of-the-art GPU-based and multi-core CPU-based parallel ACO implementations: in fact, the times obtained for the Nvidia V100 Volta GPU were up to 7.18x and 21.79x smaller, respectively. The fastest of the proposed MMAS variants is able to generate over 1 million candidate solutions per second when solving a 1,002-city instance. Moreover, we show that, combined with the 2-opt local search heuristic, the proposed parallel MMAS finds high-quality solutions for the TSP instances with up to 18,512 nodes.

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Section: Max-min Ant Systemmentioning

confidence: 99%

Section: Recent Advancementsmentioning

confidence: 99%

Section: Parallel Rwmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Implementing a GPU-based parallel MAX–MIN Ant System

Skinderowicz

2020

Future Generation Computer Systems

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“…This greatly optimizes the calculation process of the ant colony algorithm. Ant colony algorithm itself implies a certain degree of parallelism [8]. In essence, the ant colony algorithm is characterized by parallel cooperative optimization method, and the optimal solution is obtained by using parallel computation [9].…”

mentioning

confidence: 99%

A Parallelization Research for FY Satellite Rainfall Estimate Day Knock off Product Algorithm

Lin¹,

Zhao²,

Fan³

et al. 2018

ACS

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With the development of satellite remote sensing technology, more and more requirements are put forward on the timeliness and stability of the satellite weather service system. The FY satellite rainfall estimate day knock off product algorithm runs longer, about 20 minutes, which affects the estimated rainfall product generated timeliness. Research and development of parallel optimization algorithms based on the needs of satellite meteorological services and their effectiveness in practical applications are necessary ways to enhance the high-performance and high-availability capabilities of satellite meteorological services. So aiming at this problem, we started the parallel algorithm research based on the analysis of precipitation estimation algorithm. Firstly, we explained the steps of precipitation estimated date knock off product algorithm; secondly, we analyzed the four main calculation module calculating the amount of algorithms; thirdly, multithreaded parallel algorithm and MPI parallelization was designed. Finally, the multithreaded parallel and MPI parallelization were realized. Experimental results show that the multithreaded parallel and MPI parallelization algorithm could greatly improve the overall degree of computational efficiency. And, MPI parallelization mode has a higher operating efficiency. The performance of parallel processing is closely related to the architecture of the computer. From the perspective of service scheduling and product algorithms, the MPI parallelization approach is adopted to achieve the purpose of improving service quality.

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Graphic process units‐based chicken swarm optimization algorithm for function optimization problems

Lin

Zhong

Chen

2020

Concurrency and Computation

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This article focuses on how to design an efficient GPU-based chicken swarm optimization (CSO) algorithm (GCSO), so as to improve diversity and speed up convergence by running a large number of populations in parallel. GCSO mainly improves the sequential CSO in three aspects: (i) GCSO modifies the location updating equation of the rooster and proposes a parallel iterative strategy to transform the sequential iteration process into a parallel iterative process, thereby achieving fine-grained parallelism and improving the convergence speed. (ii) A multirange search strategy is proposed to build different neighborhoods for each flock on the graphic process units (GPU), so that each flock searched in their respective neighborhoods, thus increasing the density and diversity of the search, and making it not easy to fall into a local optimum. (iii) A new column storage structure is designed to meet the requirement of coalescent access on GPU. Twelve benchmark functions are selected to compare GCSO algorithm with some sequential intelligence optimization algorithms and the GPU-based particle swarm algorithm. The results show that the GCSO is able to obtain a speedup up to 163.09× compared with the CSO and achieve better optimization results in terms of both optimization accuracy and convergence speed than some intelligence optimization algorithms.

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High-throughput Ant Colony Optimization on graphics processing units

Cited by 25 publications

References 18 publications

Implementing a GPU-based parallel MAX–MIN Ant System

Implementing a GPU-based parallel MAX–MIN Ant System

A Parallelization Research for FY Satellite Rainfall Estimate Day Knock off Product Algorithm

Graphic process units‐based chicken swarm optimization algorithm for function optimization problems

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