Desynchronization in distributed Ant Colony Optimization in HPC environment

Starzec, Mateusz; Starzec, Grażyna; Byrski, Aleksander; Turek, Wojciech; Piętak, Kamil

doi:10.1016/j.future.2020.03.045

Cited by 20 publications

(12 citation statements)

References 19 publications

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“…As we have shown in [18], ACO can reach good scalability for up to hundreds of nodes in an HPC environment. The proposed architecture keeps the pheromone matrix parts distributed across all nodes and uses desynchronized updates in order to achieve good scalability without a noticeable deterioration of the result quality.…”

Section: Scalability In Computationsmentioning

confidence: 78%

“…The impact of desynchronization on the results is negligible and might be considered as another randomization factor. On the other hand, the speedup of the algorithm allows it to compute many more possible solutions of a problem, which leads to increased exploration and improved final results [18]. The speedup also enables faster optimization thanks to extensive parallelization.…”

Section: Resultsmentioning

confidence: 99%

“…As computation clusters and HPC environments become more accessible, the horizontal scalability of algorithms is becoming more and more important. A lot of articles have been published regarding the scalability of both optimization algorithms [17,18] and simulations [2,13,14]. However, the majority of this research focuses on multi-processor single-node scalability [1,7] or, at most, dozens of computational nodes [8,11].…”

Section: Scalability In Computationsmentioning

confidence: 99%

“…In the previous publication [18], we described and tested the desynchronized version of ant colony optimization. The idea of the algorithm was to remove the synchronization point at the end of each iteration (when the ants update the pheromone matrix).…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

See 3 more Smart Citations

Desynchronization of simulation and optimization algorithms in HPC Environment

Starzec

Paciorek

2020

csci

Self Cite

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Need for scalability of an algorithm is essential, when one wants to utilize HPC infrastructure in an efficient and reasonable way. In such infrastructures, synchronization affects the efficiency of the parallel algorithms. However, one can consider introducing certain means of desynchronization in order to increase scalability. Allowing for omitting or delaying certain messages, can be easily accepted in the case of metaheuristics. Furthermore, some simulations can also follow this pattern and handle bigger environments. The paper presents a short survey of desynchronization idea, pointing out already obtained results or sketching out the future work focused on scaling the parallel and distributed computing or simulation algorithms leveraging desynchronization.

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Section: Scalability In Computationsmentioning

confidence: 78%

Section: Resultsmentioning

confidence: 99%

Section: Scalability In Computationsmentioning

confidence: 99%

Section: Ant Colony Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

Desynchronization of simulation and optimization algorithms in HPC Environment

Starzec

Paciorek

2020

csci

Self Cite

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“…Metaheuristics can be parallelized in a number of ways [27,28], and there are works on parallelization of each of the basic metaheuristics considered (Local Search [29], Tabu Search [30], Genetic Algorithm [31], Ant Colony [32], ...), and for different types of computational systems (e.g., CMP architectures [33], distributed platforms [34] and GPUs [35,36]). On the other hand, the unified, parameterized schema enables the simultaneous implementation of parallel versions of different basic metaheuristics and their hybridations for different types of computational systems (e.g., shared-memory [11], heterogeneous clusters [12] and many-core systems like GPUs [37]).…”

Section: Hybrid Metaheuristics On Heterogenous Multicore+multigpumentioning

confidence: 99%

Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models

et al. 2020

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In the last years, the huge amount of data available in many disciplines makes the mathematical modeling, and, more concretely, econometric models, a very important technique to explain those data. One of the most used of those econometric techniques is the Vector Autoregression Models (VAR) which are multi-equation models that linearly describe the interactions and behavior of a group of variables by using their past. Traditionally, Ordinary Least Squares and Maximum likelihood estimators have been used in the estimation of VAR models. These techniques are consistent and asymptotically efficient under ideal conditions of the data and the identification problem. Otherwise, these techniques would yield inconsistent parameter estimations. This paper considers the estimation of a VAR model by minimizing the difference between the dependent variables in a certain time, and the expression of their own past and the exogenous variables of the model (in this case denoted as VARX model). The solution of this optimization problem is approached through hybrid metaheuristics. The high computational cost due to the huge amount of data makes it necessary to exploit High-Performance Computing for the acceleration of methods to obtain the models. The parameterized, parallel implementation of the metaheuristics and the matrix formulation ease the simultaneous exploitation of parallelism for groups of hybrid metaheuristics. Multilevel and heterogeneous parallelism are exploited in multicore CPU plus multiGPU nodes, with the optimum combination of the different parallelism parameters depending on the particular metaheuristic and the problem it is applied to.

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Parallel Processing Algorithms for the Vehicle Routing Problem and Its Variants: A Literature Review with a Look into the Future

Rabbouch

Saâdaoui

2020

Algorithms and Architectures for Parallel Processing

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Desynchronization in distributed Ant Colony Optimization in HPC environment

Cited by 20 publications

References 19 publications

Desynchronization of simulation and optimization algorithms in HPC Environment

Desynchronization of simulation and optimization algorithms in HPC Environment

Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models

Parallel Processing Algorithms for the Vehicle Routing Problem and Its Variants: A Literature Review with a Look into the Future

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