An improved GPU-accelerated heuristic technique applied to the capacitated vehicle routing problem

Abdelatti, Marwan; Sodhi, Manbir

doi:10.1145/3377930.3390159

Cited by 12 publications

(2 citation statements)

References 25 publications

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“…With this other recent genetic algorithm to solve the capacitated vehicle routing problem [1], the authors used a mix of the two parallelism types, sometimes using par thread parallelism and sometimes per block. They reached a speedup of 454 with a NVIDIA GeForce RTX 2080.…”

Section: Related Workmentioning

confidence: 99%

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X-Aevol

Turpin

Gautier

Rouzaud-Cornabas

2021

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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X-Aevol is the GPU port of the Aevol model, a bio-inspired genetic algorithm designed to study the evolution of micro-organisms and its effects on their genome structure. This model is used for in-silico experimental evolution that requires the computation of populations of thousands of individuals during tens of millions of generations. As the model is extended with new features and experiments are conducted with larger populations, computational time becomes prohibitive.X-Aevol is a response to the need of more computational power. It was designed to leverage the massive parallelization capabilities of GPU. As Aevol exposes an irregular and dynamic computational pattern, it was not a straightforward process to adapt it for massively parallel architectures. In this paper, we present how we have adapted the Aevol underlying algorithms to GPU architectures. We implement our new algorithms with CUDA programming language and test them on a representative benchmark of Aevol workloads. To conclude, we present our performance evaluation on NVIDIA Tesla V100 and A100. We show how we reach a speed-up of 1,000 over a sequential execution on a CPU and the speed-up gain up to 50% from using the newer Ampere micro-architecture in comparison with Volta one. CCS CONCEPTS• Computing methodologies → Massively parallel algorithms; Artificial life; Genetic algorithms.

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Section: Related Workmentioning

confidence: 99%

“…However, an important difference has to be emphasised. Classical genetic algorithms [1,12,24,25] are designed to solve a given useful problem. For Aevol (and other in-silico experimental evolution software), the final result is not important.…”

Section: Introductionmentioning

confidence: 99%