Re-engineering the ant colony optimization for CMP architectures

Abstract

[EN] The ant colony optimization (ACO) is inspired by the behavior of real ants, and as a bioinspired method, its underlying computation is massively parallel by definition. This paper shows re-engineering strategies to migrate the ACO algorithm applied to the Traveling Salesman Problem to modern Intel-based multi- and many-core architectures in a step-by-step methodology. The paper provides detailed guidelines on how to optimize the algorithm for the intra-node (thread and vector) parallelization, showing the performance scalability along with the number of cores on different Intel architectures, reporting up to 5.5x speedup factor between the Intel Xeon Phi Knights Landing and Intel Xeon v2. Moreover, parallel efficiency is provided for all targeted architectures, finding that core load imbalance, memory bandwidth limitations, and NUMA effects on data placement are some of the key factors limiting performance. Finally, a distributed implementation is also presented, reaching up to 2.96x speedup factor when running the code on 3 nodes over the single-node counterpart version. In the latter case, the parallel efficiency is affected by the synchronization frequency, which also affects the quality of the solution found by the distributed implementation.This work was partially supported by the Fundación Séneca, Agencia de Ciencia y Tecnología de la Región de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities as well as European Commission FEDER funds under Grants TIN2015-66972-C5-3-R, RTI2018-098156-B-C53, TIN2016-78799-P (AEI/FEDER, UE), and RTC-2017-6389-5. We acknowledge the excellent work done by Victor Montesinos while he was doing a research internship supported by the University of Murcia.Cecilia-Canales, JM.; García Carrasco, JM. (2020). Re-engineering the ant colony optimization for CMP architectures. The Journal of Supercomputing (Online). 76(6):4581-4602. https://doi.org/10.1007/s11227-019-02869-8S45814602766Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, LebanonAkila M, Anusha P, Sindhu M, Selvan Krishnasamy T (2017) Examination of PSO, GA-PSO and ACO algorithms for the design optimization of printed antennas. In: IEEE Applied Electromagnetics Conference (AEMC)Dorigo M, Stützle T (2004) Ant colony optimization. A bradford book. The MIT Press, CambridgeCecilia JM, García JM, Nisbet A, Amos M, Ujaldón M (2013) Enhancing data parallelism for ant colony optimization on GPUs. J Parallel Distrib Comput 73(1):42–51Dawson L, Stewart I (2013) Improving ant colony optimization performance on the GPU using CUDA. In: IEEE Conference on Evolutionary Computation, pp 1901–1908Llanes A, Cecilia JM, Sánchez A, García JM, Amos M, Ujaldón M (2016) Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization. Cluster Comput 19(1):1–11Cecilia JM, Llanes A, Abellán JL, Gómez-Luna J, Chang L, Hwu WW (2018) High-throughput ant colony optimization on graphics processing units. J Parallel Distrib Comput 113:261–274Lloyd H, Amos M (2016) A Highly Parallelized and Vectorized Implementation of Max–Min Ant System on Intel Xeon Phi. In: IEEE computational intelligenceTirado F, Barrientos RJ, González P, Mora M (2017) Efficient exploitation of the Xeon Phi architecture for the ant colony optimization (ACO) metaheuristic. J Supercomput 73(11):5053–5070Montesinos V, García JM (2018) Vectorization strategies for ant colony optimization on intel architectures. Parallel Computing is Everywhere. IOS Press, Amsterdam, pp 400–409Lawler E, Lenstra J, Kan A, Shmoys D (1987) The Traveling salesman problem. Wiley, New YorkMontesinos V (June 2018) Performance analysis of ant colony optimization on intel architectures. Master’s Thesis, University of Murcia (Spain)Lloyd H, Amos M (2017) Analysis of independent roulette selection in parallel ant colony optimization. In: Genetic and Evolutionary Computation Conference, ACM, pp 19–26Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. Thesis, Politecnico di Milano, ItalyDuran A, Klemm M (2012) The intel many integrated core architecture. In: Internal Conference on High Performance Computing and Simulation (HPCS), pp 365–366The OpenMP API specification for parallel programming. URL: https://www.openmp.org . [Last accessed 14 June 2018]The Message Passing Interface (MPI) standard. URL: http://www.mcs.anl.gov/research/projects/mpi/ . [Last accessed 15 June 2018]Vladimirov A, Asai R (2016) Clustering modes in Knights landing processors: developer’s guide. Colfax international. URL: https://colfaxresearch.com/knl-numa/ . [Last accessed: 16 June 2018]Intel Developer Zone. URL: https://software.intel.com/en-us/modern-code . [Last accessed 02 Oct 2018]Pearce M (2018) What is code modernization? Intel developer zone. URL: http://software.intel.com/en-us/articles/what-is-code-modernization . [Last accessed 15 Feb 2018]Stützle T ACOTSP v1.03. Last accessed 15 Feb 2018. URL: http://iridia.ulb.ac.be/~mdorigo/ACO/downloads/ACOTSP-1.03.tgzReinelt G (1991) TSPLIB—a traveling salesman problem library. ORSA J Comput 3:376–384Crainic TG, Toulouse M (2003) Parallel strategies for meta-heuristics. State-of-the-art handbook in metaheuristics. Kluwer Academic Publishers, Dordrecht, pp 475–513Delévacq A, Delisle P, Gravel M, Krajecki M (2013) Parallel ant colony optimization on graphics processing units. J Parallel Distrib Comput 73(1):52–61Skinderowicz R (2016) The GPU-based parallel ant colony system. J Parallel Distrib Comput 98:48–60Zhou Y, He F, Hou N, Qiu Y (2018) Parallel ant colony optimization on multi-core SIMD CPUs. Future Gener Comput Syst 79:473–487Peake J, Amos M, Yiapanis P, Lloyd H (2018) Vectorized candidate set selection for parallel ant colony optimization. In: Genetic and Evolutionary Computation Conference, ACM, pp 1300–1306Stützle T (1998) Parallelization strategies for ant colony optimization. In: Eiben AE, Bäck T, Schoenauer M, Schwefel HP (eds) Parallel problem solving from nature—PPSN V. PPSN. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, HeidelbergAbdelkafi O, Lepagnot J, Idoumghar L (2014) Multi-level parallelization for hybrid ACO. In: Siarry P, Idoumghar L, Lepagnot J (eds) Swarm Intelligence Based Optimization. ICSIBO 2014. Lecture Notes in Computer Science, vol 8472. Springer, ChamMichel R, Middendorf M (1998) An island model based ant system with lookahead for the shortest super sequence problem. In: Eiben AE, Bäck T, Schoenauer M, Schwefel HP (eds) Parallel problem solving from nature— PPSN V. PPSN. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, HeidelbergChen L, Sun H, Wang S (2008) Parallel implementation of ant colony optimization on MPP. In: International Conference on Machine Learning and CyberneticsLin Y, Cai H, Xiao J, Zhang J (2007) Pseudo parallel ant colony optimization for continuous functions. In: International Conference on Natural Computatio