article

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

Authors:

Alécio Binotto,

Joanna Kołodziej,

Ivona BrandicAuthors Info & Claims

Computing, Volume 101, Issue 8

Pages 893 - 936

https://doi.org/10.1007/s00607-018-0614-9

Published: 01 August 2019 Publication History

Abstract

While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software execution on parallel computing systems demands consideration of many parameters at compile-time and run-time. Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

References

[1]

Agakov F, Bonilla E, Cavazos J, Franke B, Fursin G, O'Boyle MF, Thomson J, Toussaint M, Williams CK (2006) Using machine learning to focus iterative optimization. In: Proceedings of the international symposium on code generation and optimization, IEEE Computer Society, pp 295---305

Digital Library

[2]

Ahmad I, Kwok Y, Ahmad I, Dhodhi M (2001) Scheduling parallel programs using genetic algorithms. Solutions to parallel and distributed computing problems. Wiley, New York, pp 231---254

[3]

Aho AV, Lam MS, Sethi R, Ullman JD (2006) Compilers: principles, techniques, and tools, 2nd edn. Addison-Wesley Longman Publishing Co., Inc, Boston

Digital Library

[4]

Albayrak OE, Akturk I, Ozturk O (2013) Improving application behavior on heterogeneous manycore systems through kernel mapping. Parallel Comput 39(12):867---878

Digital Library

[5]

Ansel J, Chan C, Wong YL, Olszewski M, Zhao Q, Edelman A, Amarasinghe S (2009) PetaBricks: a language and compiler for algorithmic choice. ACM, New York

Digital Library

[6]

Barney B et al (2010) Introduction to parallel computing. Lawrence Livermore National Laboratory, Livermore, p 10

[7]

Beach TH, Avis NJ (2009) An intelligent semi-automatic application porting system for application accelerators. In: Proceedings of the combined workshops on UnConventional high performance computing workshop plus memory access workshop, ACM, pp 7---10

Digital Library

[8]

Beck F, Koch S, Weiskopf D (2016) Visual analysis and dissemination of scientific literature collections with survis. IEEE Trans Visual Comput Graphics 22(1):180---189.

[9]

Benkner S, Pllana S, Träff JL, Tsigas P, Richards A, Namyst R, Bachmayer B, Kessler C, Moloney D, Sanders P (2011) The PEPPHER approach to programmability and performance portability for heterogeneous many-core architectures. In: ParCo

[10]

Biernacki P, Waldorf D (1981) Snowball sampling: problems and techniques of chain referral sampling. Sociol Methods Res 10(2):141---163

[11]

Binotto APD, Wehrmeister MA, Kuijper A, Pereira CE (2013) Sm@rtConfig: a context-aware runtime and tuning system using an aspect-oriented approach for data intensive engineering applications. Control Eng Pract 21(2):204---217

[12]

Braun TD, Siegel HJ, Beck N, Bölöni LL, Maheswaran M, Reuther AI, Robertson JP, Theys MD, Yao B, Hensgen D et al (2001) A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J Parallel Distrib Comput 61(6):810---837

Digital Library

[13]

Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gen Comp Syst 25(6):599---616.

Digital Library

[14]

Carretero J, Xhafa F, Abraham A (2007) Genetic algorithm based schedulers for grid computing systems. Int J Innov Comput Inf Control 3(6):1---19

[15]

Castro M, Goes LFW, Ribeiro CP, Cole M, Cintra M, Mehaut JF (2011) A machine learning-based approach for thread mapping on transactional memory applications. In: 2011 18th International conference on high performance computing (HiPC), IEEE, pp 1---10

Digital Library

[16]

Castro M, Góes LFW, Fernandes LG, Méhaut JF (2012) Dynamic thread mapping based on machine learning for transactional memory applications. In: Euro-Par 2012 Parallel Processing, Springer, pp 465---476

Digital Library

[17]

Cavazos J, Moss JEB (2004) Inducing heuristics to decide whether to schedule. In: Conference on programming language design and implementation, ACM, New York, NY, USA, PLDI '04, pp 183---194

Digital Library

[18]

Cavazos J, Fursin G, Agakov F, Bonilla E, Boyle MF, Temam O (2007) Rapidly selecting good compiler optimizations using performance counters. In: International symposium on code generation and optimization, 2007. CGO'07. IEEE, pp 185---197

Digital Library

[19]

Chen X, Long S (2009) Adaptive multi-versioning for OpenMP parallelization via machine learning. In: 15th International conference on parallel and distributed systems (ICPADS), 2009, IEEE, pp 907---912

Digital Library

[20]

Chirkin AM, Belloum AS, Kovalchuk SV, Makkes MX, Melnik MA, Visheratin AA, Nasonov DA (2017) Execution time estimation for workflow scheduling. Future generation computer systems.

[21]

Cooper KD, Grosul A, Harvey TJ, Reeves S, Subramanian D, Torczon L, Waterman T (2005) ACME: adaptive compilation made efficient. In: ACM SIGPLAN notices, ACM 40:69---77

Digital Library

[22]

Corbalan J, Martorell X, Labarta J (2005) Performance-driven processor allocation. IEEE Trans Parallel Distrib Syst 16(7):599---611

Digital Library

[23]

Danylenko A, Kessler C, Löwe W (2011) Comparing machine learning approaches for context-aware composition. In: Software composition, Springer, Berlin pp 18---33

Digital Library

[24]

Diamos GF, Yalamanchili S (2008) Harmony: An execution model and runtime for heterogeneous many core systems. In: Proceedings of the 17th international symposium on high performance distributed Computing, ACM, New York, NY, USA, HPDC '08, pp 197---200.

Digital Library

[25]

Diefendorff K (1999) Power4 focuses on memory bandwidth. Microprocess. Rep. 13(13):1---8

[26]

Dongarra J, Sterling T, Simon H, Strohmaier E (2005) High-performance computing: clusters, constellations, mpps, and future directions. Comput. Sci. Eng. 7(2):51---59

Digital Library

[27]

Duda RO, Hart PE et al (1973) Pattern classification and scene analysis, vol 3. Wiley, New York

[28]

Eastep J, Wingate D, Santambrogio MD, Agarwal A (2010) Smartlocks: lock acquisition scheduling for self-aware synchronization. In: Proceedings of the 7th international conference on autonomic computing, ACM, pp 215---224

Digital Library

[29]

Eastep J, Wingate D, Agarwal A (2011) Smart data structures: an online machine learning approach to multicore data structures. In: Proceedings of the 8th international conference on Autonomic computing, ACM, pp 11---20

Digital Library

[30]

Emani MK, Wang Z, O'Boyle MF (2013) Smart, adaptive mapping of parallelism in the presence of external workload. In: International symposium on code generation and optimization (CGO), IEEE, pp 1---10

Digital Library

[31]

Fonseca A, Cabral B (2013) ÆminiumGPU: An Intelligent Framework for GPU Programming. In: Facing the multicore-challenge III, Springer, pp 96---107

[32]

Foster I, Kesselman C (2003) The Grid 2: blueprint for a new computing infrastructure. Elsevier, Amsterdam

Digital Library

[33]

Foster I, Zhao Y, Raicu I, Lu S (2008) Cloud computing and grid computing 360-degree compared. In: 2008 Grid computing environments workshop, pp 1---10.

[34]

Fursin G, Miranda C, Temam O, Namolaru M, Yom-Tov E, Zaks A, Mendelson B, Bonilla E, Thomson J, Leather H, et al. (2008) MILEPOST GCC: machine learning based research compiler. In: GCC summit

[35]

Fursin G, Kashnikov Y, Memon AW, Chamski Z, Temam O, Namolaru M, Yom-Tov E, Mendelson B, Zaks A, Courtois E et al (2011) Milepost gcc: machine learning enabled self-tuning compiler. Int J Parallel Prog 39(3):296---327

[36]

Gaussier E, Glesser D, Reis V, Trystram D (2015) Improving backfilling by using machine learning to predict running times. In: Proceedings of the international conference for high performance computing, networking, storage and analysis, ACM, p 64

Digital Library

[37]

Geer D (2005) Chip makers turn to multicore processors. Computer 38(5):11---13.

Digital Library

[38]

Gordon MI, Thies W, Amarasinghe S (2006) Exploiting coarse-grained task, data, and pipeline parallelism in stream programs. In: ACM SIGOPS Operating Systems Review, ACM 40:151---162

Digital Library

[39]

Gould N (2006) An introduction to algorithms for continuous optimization. Oxford University Computing Laboratory Notes

[40]

Grewe D, OBoyle MF (2011) A static task partitioning approach for heterogeneous systems using opencl. In: Compiler Construction, Springer, Berlin, pp 286---305

Digital Library

[41]

Grewe D, Wang Z, O'Boyle MF (2011) A workload-aware mapping approach for data-parallel programs. In: Proceedings of the 6th international conference on high performance and embedded architectures and compilers, ACM, pp 117---126

Digital Library

[42]

Gropp W, Lusk E, Skjellum A (1999) Using MPI: portable parallel programming with the message-passing interface, vol 1. MIT press, Cambridge

Digital Library

[43]

Grzonka D, Kolodziej J, Tao J (2014) Using artificial neural network for monitoring and supporting the grid scheduler performance. In: ECMS, pp 515---522

[44]

Grzonka D, Jakbik A, KoÅodziej J, Pllana S (2017) Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security. Future Generation Computer Systems. http://www.sciencedirect.com/science/article/pii/S0167739X17310531

[45]

Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157---1182

Digital Library

[46]

Hoffmann H, Eastep J, Santambrogio MD, Miller JE, Agarwal A (2010a) Application heartbeats: a generic interface for specifying program performance and goals in autonomous computing environments. In: Parashar M, Figueiredo RJO, Kiciman E (eds) ICAC. ACM, New York City

Digital Library

[47]

Hoffmann H, Maggio M, Santambrogio MD, Leva A, Agarwal A (2010b) SEEC: A framework for self-aware computing. http://hdl.handle.net/1721.1/59519

[48]

Iakymchuk R, Jordan H, Bo Peng I, Markidis S, Laure E (2016) A particle-in-cell method for automatic load-balancing with the allscale environment. In: The Exascale applications & Software conference (EASC2016)

[49]

Jeffers J, Reinders J (2015) High Performance Parallelism Pearls Volume Two: Multicore and Many-core Programming Approaches. Morgan Kaufmann, Burlington

Digital Library

[50]

Jin C, de Supinski BR, Abramson D, Poxon H, DeRose L, Dinh MN, Endrei M, Jessup ER (2016) A survey on software methods to improve the energy efficiency of parallel computing. In: The international journal of high performance computing applications p 1094342016665471.

Digital Library

[51]

Kessler C, Löwe W (2012) Optimized composition of performance-aware parallel components. Concurr Comput Pract Exp 24(5):481---498

Digital Library

[52]

Kessler C, Dastgeer U, Thibault S, Namyst R, Richards A, Dolinsky U, Benkner S, Träff JL, Pllana S (2012) Programmability and performance portability aspects of heterogeneous multi-/manycore systems. In: Design, automation & test in Europe conference & exhibition (DATE), 2012, IEEE, pp 1403---1408

Digital Library

[53]

Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. In: Technical Report EBSE 2007-001, Keele University and Durham University Joint Report

[54]

Lee BD, Schopf JM (2003) Run-time prediction of parallel applications on shared environments. In: IEEE International conference on cluster computing, 2003. Proceedings. 2003, IEEE, pp 487---491

[55]

Li L, Dastgeer U, Kessler C (2012) Adaptive off-line tuning for optimized composition of components for heterogeneous many-core systems. In: High performance computing for computational science-VECPAR 2012, Springer, pp 329---345

[56]

Li M, Zeng L, Meng S, Tan J, Zhang L, Butt AR, Fuller N (2014) Mronline: Mapreduce online performance tuning. In: Proceedings of the 23rd international symposium on High-performance parallel and distributed computing, ACM, pp 165---176

Digital Library

[57]

Liu B, Zhao Y, Zhong X, Liang Z, Feng B (2013) A Novel Thread Partitioning Approach Based on Machine Learning for Speculative Multithreading. In: IEEE international conference on embedded and ubiquitous computing high performance computing and communications & 2013 (HPCC_EUC), 2013 IEEE 10th International Conference on, IEEE, pp 826---836

[58]

Luk CK, Hong S, Kim H (2009) Qilin: Exploiting parallelism on heterogeneous multiprocessors with adaptive mapping. In: Proceedings of the 42nd annual IEEE/ACM international symposium on microarchitecture, ACM, New York, NY, USA, MICRO 42, pp 45---55.

Digital Library

[59]

Malawski M, Juve G, Deelman E, Nabrzyski J (2015) Algorithms for cost- and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48:1---18. special Section: Business and Industry Specific Cloud

Digital Library

[60]

Mantripragada K, Binotto APD, Tizzei LP (2014) A self-adaptive auto-scaling method for scientific applications on HPC environments and clouds. CoRR abs/1412.6392

[61]

Mastelic T, Fdhila W, Brandic I, Rinderle-Ma S (2015) Predicting resource allocation and costs for business processes in the cloud. In: 2015 IEEE world congress on services, pp 47---54.

Digital Library

[62]

Memeti S, Pllana S (2016a) Combinatorial optimization of dna sequence analysis on heterogeneous systems. Concurrency and computation: practice and experience pp n/a-n/a. cpe.4037

[63]

Memeti S, Pllana S (2016b) Combinatorial optimization of work distribution on heterogeneous systems. In: 2016 45th international conference on parallel processing workshops (ICPPW), pp 151---160.

[64]

Memeti S, Pllana S (2016c) A machine learning approach for accelerating dna sequence analysis. The International Journal of High Performance Computing Applications 0(0):1094342016654,214.

[65]

Memeti S, Pllana S, Kołodziej J (2016) Optimal worksharing of DNA sequence analysis on accelerated platforms. Springer, Cham, pp 279---309.

[66]

Memeti S, Li L, Pllana S, Kolodziej J, Kessler C (2017) Benchmarking opencl, openacc, openmp, and cuda: Programming productivity, performance, and energy consumption. In: Proceedings of the 2017 workshop on adaptive resource management and scheduling for cloud computing, ACM, New York, NY, USA, ARMS-CC '17, pp 1---6.

Digital Library

[67]

Mitchell TM (1997) Machine learning, 1st edn. McGraw-Hill Inc, New York, NY, USA

Digital Library

[68]

Mittal S, Vetter JS (2015) A survey of cpu-gpu heterogeneous computing techniques. ACM Comput Surv (CSUR) 47(4):69

Digital Library

[69]

Monsifrot A, Bodin F, Quiniou R (2002) A machine learning approach to automatic production of compiler heuristics. In: Artificial intelligence: methodology, systems, and applications, Springer, pp 41---50

Digital Library

[70]

Nvidia C (2015) CUDA C programming guide. NVIDIA Corp 120:18

[71]

Ogilvie W, Petoumenos P, Wang Z, Leather H (2015) Intelligent heuristic construction with active learning. In: Compilers for parallel computing (CPC'15). London, United Kingdom

[72]

OpenMP A (2013) OpenMP 4.0 specification, June 2013

[73]

Padua D (2011) Encyclopedia of parallel computing. Springer, Berlin

Digital Library

[74]

Page AJ, Naughton TJ (2005a) Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing. In: 19th International parallel and distributed processing symposium, IEEE, pp 189a---189a

Digital Library

[75]

Page AJ, Naughton TJ (2005b) Framework for task scheduling in heterogeneous distributed computing using genetic algorithms. Artif Intell Rev 24(3):415---429.

Digital Library

[76]

Park Yw, Baskiyar S, Casey K (2010) A novel adaptive support vector machine based task scheduling. In: Proceedings the 9th International Conference on Parallel and Distributed Computing and Networks, Austria, pp 16---18

[77]

Pekhimenko G, Brown AD (2011) Efficient program compilation through machine learning techniques. In: Software Automatic Tuning, Springer, pp 335---351

[78]

Pllana S, Benkner S, Mehofer E, Natvig L, Xhafa F (2008) Towards an intelligent environment for programming multi-core computing systems. Euro-Par Workshops, Springer, Lecture Notes in Computer Science 5415:141---151

Digital Library

[79]

Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) Numerical recipes 3rd edition: the art of scientific computing, 3rd edn. Cambridge University Press, Cambridge

Digital Library

[80]

Ravi VT, Agrawal G (2011) A dynamic scheduling framework for emerging heterogeneous systems. In: 18th International conference on high performance computing (HiPC), 2011, IEEE, pp 1---10

Digital Library

[81]

Rossbach CJ, Yu Y, Currey J, Martin JP, Fetterly D (2013) Dandelion: a compiler and runtime for heterogeneous systems. In: Proceedings of the twenty-fourth ACM symposium on operating systems principles, ACM, pp 49---68

Digital Library

[82]

Sadashiv N, Kumar SMD (2011) Cluster, grid and cloud computing: A detailed comparison. In: 2011 6th International conference on computer science education (ICCSE), pp 477---482.

[83]

Sandrieser M, Benkner S, Pllana S (2012) Using explicit platform descriptions to support programming of heterogeneous many-core systems. Parallel Comput 38(1---2):52---56

Digital Library

[84]

Silvano C, Agosta G, Cherubin S, Gadioli D, Palermo G, Bartolini A, Benini L, Martinoviă? J, Palkoviă? M, Slaninová K, Bispo Ja, Cardoso JaMP, Abreu R, Pinto P, Cavazzoni C, Sanna N, Beccari AR, Cmar R, Rohou E (2016) The antarex approach to autotuning and adaptivity for energy efficient hpc systems. In: Proceedings of the international conference on computing frontiers, ACM, New York, NY, USA, CF '16, pp 288---293.

Digital Library

[85]

Sivanandam SN, Visalakshi P (2009) Dynamic task scheduling with load balancing using parallel orthogonal particle swarm optimisation. Int J Bio-Inspired Comput 1(4):276---286.

Digital Library

[86]

Smanchat S, Indrawan M, Ling S, Enticott C, Abramson D (2013) Scheduling parameter sweep workflow in the grid based on resource competition. Future Gen Comput Syst 29(5):1164---1183.

Digital Library

[87]

Stephenson M, Amarasinghe S (2005) Predicting unroll factors using supervised classification. In: International Symposium on code generation and optimization, 2005. CGO 2005, IEEE, pp 123---134

Digital Library

[88]

Stephenson M, Amarasinghe S, Martin M, O'Reilly UM (2003) Meta optimization: improving compiler heuristics with machine learning. SIGPLAN Not 38(5):77---90

Digital Library

[89]

Sterling T, Becker DJ, Savarese D, Dorband JE, Ranawake UA, Packer CV (1995) Beowulf: A parallel workstation for scientific computation. In: Proceedings of the 24th international conference on parallel processing, pp 11---14

[90]

Stone JE, Gohara D, Shi G (2010) OpenCL: a parallel programming standard for heterogeneous computing systems. Comput Sci Eng 12(1---3):66---73

Digital Library

[91]

Thomas N, Tanase G, Tkachyshyn O, Perdue J, Amato NM, Rauchwerger L (2005) A framework for adaptive algorithm selection in STAPL. In: Proceedings of the tenth ACM SIGPLAN symposium on principles and practice of parallel programming, ACM, pp 277---288

Digital Library

[92]

Tiwari A, Hollingsworth JK (2011) Online adaptive code generation and tuning. In: 2011 IEEE international parallel distributed processing symposium, pp 879---892.

Digital Library

[93]

Tiwari A, Chen C, Chame J, Hall M, Hollingsworth JK (2009) A scalable auto-tuning framework for compiler optimization. In: Proceedings of the 2009 IEEE international symposium on parallel & distributed processing, IEEE Computer Society, Washington, DC, USA, IPDPS '09, pp 1---12.

Digital Library

[94]

TOP500 (2016) TOP500 Supercomputer Sites. http://www.top500.org/. Accessed Jan 2016

[95]

Tournavitis G, Wang Z, Franke B, O'Boyle MF (2009) Towards a holistic approach to auto-parallelization: integrating profile-driven parallelism detection and machine-learning based mapping. In: ACM Sigplan notices 44:177---187

Digital Library

[96]

Viebke A, Pllana S (2015) The potential of the intel (r) xeon phi for supervised deep learning. In: 2015 IEEE 17th international conference on high performance computing and communications (HPCC), pp 758---765.

Digital Library

[97]

Voss M, Kim W (2011) Multicore desktop programming with intel threading building blocks. IEEE Softw 28(1):23---31.

Digital Library

[98]

Wang Z, O'Boyle MF (2009) Mapping parallelism to multi-cores: a machine learning based approach. In: ACM Sigplan notices, ACM 44:75---84

Digital Library

[99]

Wang Z, O'boyle MF (2013) Using machine learning to partition streaming programs. ACM Trans Archit Code Optim (TACO) 10(3):20

Digital Library

[100]

Wienke S, Springer P, Terboven C, an Mey D (2012) Openacc: First experiences with real-world applications. In: Proceedings of the 18th international conference on parallel processing, Springer-Verlag, Berlin, Heidelberg, Euro-Par'12, pp 859---870

Digital Library

[101]

Wolsey LA, Nemhauser GL (2014) Integer and combinatorial optimization. Wiley, Hoboken

[102]

Zhang Y, Burcea M, Cheng V, Ho R, Voss M (2004) An adaptive openmp loop scheduler for hyperthreaded smps. In: ISCA PDCS, pp 256---263

[103]

Zhang Y, Voss M, Rogers E (2005) Runtime empirical selection of loop schedulers on hyperthreaded smps. In: Proceedings of 19th IEEE International parallel and distributed processing symposium, 2005, IEEE, pp 44b---44b

Digital Library

[104]

Zomaya AY, Teh YH (2001) Observations on using genetic algorithms for dynamic load-balancing. IEEE Trans Parallel Distrib Syst 12(9):899---911

Digital Library

[105]

Zomaya AY, Lee RC, Olariu S (2001) An introduction to genetic-based scheduling in parallel processor systems. Solutions to Parallel and Distributed Computing Problems pp 111---133

Cited By

Tao HAbba SAl-Areeq ATangang FSamantaray SSahoo ASiqueira HMaroufpoor SDemir VDhanraj Bokde NGoliatt LJamei MAhmadianfar IBhagat SHalder BGuo THelman DAli MSattar SAl-Khafaji ZShahid SYaseen Z(2024)Hybridized artificial intelligence models with nature-inspired algorithms for river flow modelingEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107559129:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107559
Chen LMa LLi L(2024)Enhancing sine cosine algorithm based on social learning and elite opposition-based learningComputing10.1007/s00607-024-01256-3106:5(1475-1517)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s00607-024-01256-3
Stupar IHuljenic D(2023)Model-based cloud service deployment optimisation method for minimisation of application service operational costJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00389-812:1Online publication date: 18-Feb-2023
https://dl.acm.org/doi/10.1186/s13677-023-00389-8
Show More Cited By

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review
1. General and reference
  1. Cross-computing tools and techniques
2. Networks

Recommendations

Essential Particle Swarm Optimization queen with Tabu Search for MKP resolution

The Particle Swarm Optimization (PSO) algorithm is an innovative and promising optimization technique in evolutionary computation. The Essential Particle Swarm Optimization queen (EPSOq) is one of the recent discrete PSO versions that further simplifies ...
A Review of Machine Learning and Meta-heuristic Methods for Scheduling Parallel Computing Systems
LOPAL '18: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications

Optimized software execution on parallel computing systems demands consideration of many parameters at run-time. Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers ...
Localization Optimization in WSNs Using Meta-Heuristics Optimization Algorithms: A Survey
Abstract
In Wireless Sensor Networks, node localization is one of the most important system parameters. Determining the exact position of nodes in these networks is one of vital and tedious tasks. This paper presents a review of the most localization ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Computing

Computing Volume 101, Issue 8

August 2019

324 pages

ISSN:0010-485X

Issue’s Table of Contents

Copyright © Copyright © 2019 Springer-Verlag GmbH Austria, part of Springer Nature.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 August 2019

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tao HAbba SAl-Areeq ATangang FSamantaray SSahoo ASiqueira HMaroufpoor SDemir VDhanraj Bokde NGoliatt LJamei MAhmadianfar IBhagat SHalder BGuo THelman DAli MSattar SAl-Khafaji ZShahid SYaseen Z(2024)Hybridized artificial intelligence models with nature-inspired algorithms for river flow modelingEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107559129:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107559
Chen LMa LLi L(2024)Enhancing sine cosine algorithm based on social learning and elite opposition-based learningComputing10.1007/s00607-024-01256-3106:5(1475-1517)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s00607-024-01256-3
Stupar IHuljenic D(2023)Model-based cloud service deployment optimisation method for minimisation of application service operational costJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00389-812:1Online publication date: 18-Feb-2023
https://dl.acm.org/doi/10.1186/s13677-023-00389-8
ElGhondakly RMoussa SBadr N(2023)Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniquesApplied Soft Computing10.1016/j.asoc.2023.110430143:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110430
Hakimi YBaghdadi RChallal Y(2023)A Hybrid Machine Learning Model for Code OptimizationInternational Journal of Parallel Programming10.1007/s10766-023-00758-551:6(309-331)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1007/s10766-023-00758-5
Cao HLi HWang MHuang BSun Y(2022)A structural reanalysis assisted harmony search for the optimal design of structuresComputers and Structures10.1016/j.compstruc.2022.106844270:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.compstruc.2022.106844
Zhao QWu JWu XFan JWang LWu F(2022)Design and Implementation of Deep Learning Real-Time Streaming Video Data Processing SystemSmart Computing and Communication10.1007/978-3-031-28124-2_1(1-10)Online publication date: 18-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-28124-2_1
Memeti SPllana S(2021)Optimization of heterogeneous systems with AI planning heuristics and machine learning: a performance and energy aware approachComputing10.1007/s00607-021-01017-6103:12(2943-2966)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s00607-021-01017-6
Wu YYu SLai KChhabra AChang HHuang K(2020)Two-level utilization-based processor allocation for scheduling moldable jobsThe Journal of Supercomputing10.1007/s11227-020-03246-676:12(10212-10239)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s11227-020-03246-6
Stupar IHuljenić D(2019)Model-Based Extraction of Knowledge about the Effect of Cloud Application Context on Application Service Cost and Quality of ServiceScientific Programming10.1155/2019/50754122019Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1155/2019/5075412

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents