ZAKI: A Smart Method and Tool for Automatic Performance Optimization of Parallel SpMV Computations on Distributed Memory Machines
SpMV is a vital computing operation of many scientific, engineering, economic and social applications, increasingly being used to develop timely intelligence for the design and management of smart societies. Several factors affect the performance of SpMV computations, such as matrix characteristics,...
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| Published in: | Mobile networks and applications Vol. 28; no. 2; pp. 744 - 763 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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New York
Springer US
01.04.2023
Springer Nature B.V |
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| ISSN: | 1383-469X, 1572-8153 |
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| Abstract | SpMV is a vital computing operation of many scientific, engineering, economic and social applications, increasingly being used to develop timely intelligence for the design and management of smart societies. Several factors affect the performance of SpMV computations, such as matrix characteristics, storage formats, software and hardware platforms. The complexity of the computer systems is on the rise with the increasing number of cores per processor, different levels of caches, processors per node and high speed interconnect. There is an ever-growing need for new optimization techniques and efficient ways of exploiting parallelism. In this paper, we propose ZAKI, a data-driven, machine-learning approach and tool, to predict the optimal number of processes for SpMV computations of an arbitrary sparse matrix on a distributed memory machine. The aim herein is to allow application scientists to automatically obtain the best configuration, and hence the best performance, for the execution of SpMV computations. We train and test the tool using nearly 2000 real world matrices obtained from 45 application domains including computational fluid dynamics (CFD), computer vision, and robotics. The tool uses three machine learning methods, decision trees, random forest, gradient boosting, and is evaluated in depth. A discussion on the applicability of our proposed tool to energy efficiency optimization of SpMV computations is given. This is the first work where the sparsity structure of matrices have been exploited to predict the optimal number of processes for a given matrix in distributed memory environments by using different base and ensemble machine learning methods. |
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| AbstractList | SpMV is a vital computing operation of many scientific, engineering, economic and social applications, increasingly being used to develop timely intelligence for the design and management of smart societies. Several factors affect the performance of SpMV computations, such as matrix characteristics, storage formats, software and hardware platforms. The complexity of the computer systems is on the rise with the increasing number of cores per processor, different levels of caches, processors per node and high speed interconnect. There is an ever-growing need for new optimization techniques and efficient ways of exploiting parallelism. In this paper, we propose ZAKI, a data-driven, machine-learning approach and tool, to predict the optimal number of processes for SpMV computations of an arbitrary sparse matrix on a distributed memory machine. The aim herein is to allow application scientists to automatically obtain the best configuration, and hence the best performance, for the execution of SpMV computations. We train and test the tool using nearly 2000 real world matrices obtained from 45 application domains including computational fluid dynamics (CFD), computer vision, and robotics. The tool uses three machine learning methods, decision trees, random forest, gradient boosting, and is evaluated in depth. A discussion on the applicability of our proposed tool to energy efficiency optimization of SpMV computations is given. This is the first work where the sparsity structure of matrices have been exploited to predict the optimal number of processes for a given matrix in distributed memory environments by using different base and ensemble machine learning methods. |
| Author | Katib, Iyad Albeshri, Aiiad Usman, Sardar Mehmood, Rashid Altowaijri, Saleh M. |
| Author_xml | – sequence: 1 givenname: Sardar orcidid: 0000-0003-4698-6461 surname: Usman fullname: Usman, Sardar email: susman@stu.kau.edu.sa organization: Department of Computer Science, FCIT, King Abdulaziz University – sequence: 2 givenname: Rashid surname: Mehmood fullname: Mehmood, Rashid organization: High Performance Computing Center, King Abdulaziz University – sequence: 3 givenname: Iyad surname: Katib fullname: Katib, Iyad organization: Department of Computer Science, FCIT, King Abdulaziz University – sequence: 4 givenname: Aiiad surname: Albeshri fullname: Albeshri, Aiiad organization: Department of Computer Science, FCIT, King Abdulaziz University – sequence: 5 givenname: Saleh M. surname: Altowaijri fullname: Altowaijri, Saleh M. organization: Faculty of Computing and Information Technology, Northern Border University |
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| Cites_doi | 10.1504/IJCAT.2016.076790 10.2495/SDP-V11-N4-546-557 10.1016/j.jnca.2010.08.002 10.3390/app9050947 10.1016/j.procs.2015.08.566 10.1016/j.procs.2017.05.440 10.1007/978-3-319-63450-0_13 10.1587/transinf.2017EDP7176 10.1007/s11554-014-0442-x 10.1016/j.future.2016.12.033 10.1108/IJOPM-03-2015-0179 10.1108/17410381111149657 10.1016/j.egypro.2017.07.286 10.3390/jsan5040020 10.24138/jcomss.v13i2.373 10.1109/ACCESS.2017.2668840 10.1145/2851500 10.5402/2012/728913 10.1007/s11036-013-0489-0 10.1145/1562764.1562783 10.1007/978-3-319-94180-6_12 10.1007/978-3-319-94180-6_22 10.1109/IPDPSW.2018.00164 10.1007/978-3-319-94180-6_24 10.1145/1375527.1375558 10.1109/HPCC/SmartCity/DSS.2018.00116 10.1007/978-3-319-94180-6_15 10.1109/ICPADS.2011.91 10.1007/978-3-319-94180-6_13 10.1109/ICPPW.2014.30 10.1007/978-3-319-94180-6_29 10.1007/978-3-319-01884-3_20 10.1016/j.procs.2017.05.439 10.1007/978-3-319-94180-6_16 10.1109/IPDPSW.2017.155 10.1109/SGCF.2016.7492425 10.1109/ICPADS.2016.0120 10.1109/ONDM.2008.4578420 10.1109/MASCOT.2004.1348189 10.1109/ISMS.2010.84 10.1007/978-3-319-94180-6 10.1007/978-3-319-94180-6_4 10.1109/MuCoCoS.2013.6633600 10.1007/978-3-319-94180-6_30 |
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| Copyright | Springer Science+Business Media, LLC, part of Springer Nature 2019 Mobile Networks and Applications is a copyright of Springer, (2019). All Rights Reserved. Springer Science+Business Media, LLC, part of Springer Nature 2019. |
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| Keywords | Distributed memory Parallel computing Machine learning MPI Random forest Sparse matrix vector product (SpMV) Decision trees Sparse linear equation systems Gradient boosting |
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| References | Suma, Mehmood, Albugami, Katib, Albeshri (CR27) 2017; 109 Zappatore, Longo, Bochicchio (CR10) 2017; 13 CR39 CR38 CR37 CR36 CR35 CR32 CR31 Mehmood, Alturki, Zeadally (CR16) 2011; 34 Davis, Hu (CR49) 2011; 38 Montemayor, Pantrigo, Salgado (CR4) 2015; 10 González García, Meana-Llorián, Pelayo G-Bustelo, Cueva Lovelle, Garcia-Fernandez (CR3) 2017; 76 Triscone (CR2) 2016; 11 Aqib, Mehmood, Alzahrani, Katib, Albeshri (CR20) 2018; 18 CR5 CR9 CR48 CR47 CR46 Mehmood, Meriton, Graham, Hennelly, Kumar (CR12) 2017; 37 CR45 CR44 CR43 CR40 Bello, Mydlarz, Salamon (CR11) 2018 Yilmaz, Aktemur, Garzarán, Kamin, Kiraç (CR58) 2016; 13 Mehmood, Parker, Kwiatkowska (CR41) 2013 Mehmood, Alam, Albogami, Katib, Albeshri, Altowaijri (CR29) 2017; 5 Aliaga (CR7) 2012; 2012 Gade (CR8) 2016; 53 CR19 CR18 CR17 Mehmood, Lu (CR14) 2011; 22 CR15 CR59 CR56 CR55 CR54 Tabib, Rasheed, Priya Uteng (CR1) 2017; 122 CR53 Mehmood, Crowcroft (CR33) 2005 CR51 Mehmood, Graham (CR13) 2015; 64 CR50 Rahman (CR6) 2016; 5 Chen, Mao, Liu (CR34) 2014; 19 Muhammed (CR30) 2019; 9 CR28 CR26 CR25 CR24 CR23 CR67 CR22 CR66 CR21 CR65 Asanovic (CR52) 2009; 52 CR64 CUI, HIRASAWA, KOBAYASHI, TAKIZAWA (CR57) 2018; E101.D CR63 CR62 Mehmood (CR42) 2003 CR61 CR60 A Rahman (1318_CR6) 2016; 5 R Gade (1318_CR8) 2016; 53 1318_CR40 M Chen (1318_CR34) 2014; 19 G Triscone (1318_CR2) 2016; 11 1318_CR43 1318_CR44 R Mehmood (1318_CR12) 2017; 37 1318_CR45 1318_CR46 1318_CR47 1318_CR48 H CUI (1318_CR57) 2018; E101.D JP Bello (1318_CR11) 2018 AS Montemayor (1318_CR4) 2015; 10 1318_CR31 1318_CR32 R Mehmood (1318_CR14) 2011; 22 1318_CR35 1318_CR36 1318_CR37 1318_CR38 1318_CR39 TA Davis (1318_CR49) 2011; 38 R Mehmood (1318_CR33) 2005 C González García (1318_CR3) 2017; 76 DG Aliaga (1318_CR7) 2012; 2012 R Mehmood (1318_CR13) 2015; 64 1318_CR61 1318_CR62 1318_CR63 1318_CR64 1318_CR21 1318_CR65 1318_CR22 1318_CR66 1318_CR23 1318_CR67 1318_CR24 1318_CR25 R Mehmood (1318_CR16) 2011; 34 1318_CR26 R Mehmood (1318_CR29) 2017; 5 1318_CR28 B Yilmaz (1318_CR58) 2016; 13 T Muhammed (1318_CR30) 2019; 9 R Mehmood (1318_CR42) 2003 1318_CR9 M Zappatore (1318_CR10) 2017; 13 1318_CR5 S Suma (1318_CR27) 2017; 109 R Mehmood (1318_CR41) 2013 K Asanovic (1318_CR52) 2009; 52 1318_CR60 1318_CR50 1318_CR51 1318_CR53 MV Tabib (1318_CR1) 2017; 122 M Aqib (1318_CR20) 2018; 18 1318_CR54 1318_CR55 1318_CR56 1318_CR15 1318_CR59 1318_CR17 1318_CR18 1318_CR19 |
| References_xml | – ident: CR45 – ident: CR22 – ident: CR39 – ident: CR51 – volume: 53 start-page: 291 issue: 4 year: 2016 ident: CR8 article-title: Thermal imaging systems for real-time applications in smart cities publication-title: Int J Comput Appl Technol doi: 10.1504/IJCAT.2016.076790 – ident: CR35 – year: 2003 ident: CR42 publication-title: A Survey of Out-of-Core Analysis Techniques in Stochastic Modelling – ident: CR54 – ident: CR61 – year: 2005 ident: CR33 publication-title: Parallel iterative solution method for large sparse linear equation systems – volume: 11 start-page: 546 issue: 4 year: 2016 end-page: 557 ident: CR2 article-title: Computational fluid dynamics as a tool to predict the air pollution dispersion in a neighborhood – a research project to improve the quality of life in cities publication-title: Int J Sustain Dev Plan doi: 10.2495/SDP-V11-N4-546-557 – volume: 34 start-page: 1518 issue: 5 year: 2011 end-page: 1529 ident: CR16 article-title: Multimedia applications over metropolitan area networks (MANs) publication-title: J Netw Comput Appl doi: 10.1016/j.jnca.2010.08.002 – ident: CR25 – ident: CR21 – ident: CR46 – ident: CR19 – volume: 9 start-page: 947 issue: 5 year: 2019 ident: CR30 article-title: SURAA: A Novel Method and Tool for Loadbalanced and Coalesced SpMV Computations on GPUs publication-title: Appl Sci doi: 10.3390/app9050947 – volume: 64 start-page: 1107 year: 2015 end-page: 1114 ident: CR13 article-title: Big Data Logistics: A health-care Transport Capacity Sharing Model publication-title: Procedia Computer Science doi: 10.1016/j.procs.2015.08.566 – ident: CR67 – ident: CR15 – volume: 18 start-page: 246 issue: 12 year: 2018 end-page: 254 ident: CR20 article-title: A Deep Learning Model to Predict Vehicles Occupancy on Freeways for Traffic Management publication-title: IJCSNS - Int J Comput Sci Netw Secur – ident: CR50 – ident: CR9 – ident: CR32 – ident: CR60 – ident: CR36 – ident: CR5 – volume: 109 start-page: 1122 year: 2017 end-page: 1127 ident: CR27 article-title: Enabling Next Generation Logistics and Planning for Smarter Societies publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2017.05.440 – ident: CR64 – start-page: 373 year: 2018 end-page: 397 ident: CR11 article-title: Sound Analysis in Smart Cities publication-title: Computational Analysis of Sound Scenes and Events doi: 10.1007/978-3-319-63450-0_13 – ident: CR26 – volume: E101.D start-page: 2307 issue: 9 year: 2018 end-page: 2314 ident: CR57 article-title: A Machine Learning-Based Approach for Selecting SpMV Kernels and Matrix Storage Formats publication-title: IEICE Trans Inf Syst doi: 10.1587/transinf.2017EDP7176 – ident: CR18 – ident: CR43 – ident: CR66 – ident: CR47 – volume: 10 start-page: 723 issue: 4 year: 2015 end-page: 724 ident: CR4 article-title: Special issue on real-time computer vision in smart cities publication-title: J Real-Time Image Process doi: 10.1007/s11554-014-0442-x – volume: 76 start-page: 301 year: 2017 end-page: 313 ident: CR3 article-title: Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes publication-title: Futur Gener Comput Syst doi: 10.1016/j.future.2016.12.033 – ident: CR37 – ident: CR53 – volume: 37 start-page: 75 issue: 1 year: 2017 end-page: 104 ident: CR12 article-title: Exploring the influence of big data on city transport operations: a Markovian approach publication-title: Int J Oper Prod Manag doi: 10.1108/IJOPM-03-2015-0179 – volume: 22 start-page: 804 issue: 6 year: 2011 end-page: 817 ident: CR14 article-title: Computational Markovian analysis of large systems publication-title: J Manuf Technol Manag doi: 10.1108/17410381111149657 – volume: 38 start-page: 1 issue: 1 year: 2011 end-page: 25 ident: CR49 article-title: The university of Florida sparse matrix collection publication-title: ACM Trans Math Softw – ident: CR56 – volume: 122 start-page: 361 year: 2017 end-page: 366 ident: CR1 article-title: Methodology for assessing cycling comfort during a smart city development publication-title: Energy Procedia doi: 10.1016/j.egypro.2017.07.286 – ident: CR40 – ident: CR63 – ident: CR23 – ident: CR44 – ident: CR48 – volume: 5 start-page: 20 issue: 4 year: 2016 ident: CR6 article-title: Cloud-Enhanced Robotic System for Smart City Crowd Control publication-title: J Sens Actuator Networks doi: 10.3390/jsan5040020 – ident: CR65 – ident: CR38 – volume: 13 start-page: 53 issue: 2 year: 2017 ident: CR10 article-title: Crowd-sensing our Smart Cities: a Platform for Noise Monitoring and Acoustic Urban Planning publication-title: J Commun Softw Syst doi: 10.24138/jcomss.v13i2.373 – ident: CR17 – ident: CR31 – volume: 5 start-page: 2615 year: 2017 end-page: 2635 ident: CR29 article-title: UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2668840 – ident: CR55 – volume: 13 start-page: 1 issue: 1 year: 2016 end-page: 26 ident: CR58 article-title: Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication publication-title: ACM Trans Archit Code Optim doi: 10.1145/2851500 – ident: CR59 – volume: 2012 start-page: 1 year: 2012 end-page: 19 ident: CR7 article-title: 3D Design and Modeling of Smart Cities from a Computer Graphics Perspective publication-title: ISRN Comput Graph doi: 10.5402/2012/728913 – ident: CR28 – year: 2013 ident: CR41 publication-title: An efficient BDD-based implementation of Gauss-Seidel for CTMC analysis – ident: CR62 – volume: 19 start-page: 171 issue: 2 year: 2014 end-page: 209 ident: CR34 article-title: Big data: A survey publication-title: Mob Networks Appl doi: 10.1007/s11036-013-0489-0 – ident: CR24 – volume: 52 start-page: 56 issue: 10 year: 2009 ident: CR52 article-title: A view of the parallel computing landscape publication-title: Commun ACM doi: 10.1145/1562764.1562783 – ident: 1318_CR36 doi: 10.1007/978-3-319-94180-6_12 – ident: 1318_CR18 doi: 10.1007/978-3-319-94180-6_22 – ident: 1318_CR39 – volume: 38 start-page: 1 issue: 1 year: 2011 ident: 1318_CR49 publication-title: ACM Trans Math Softw – ident: 1318_CR50 doi: 10.1109/IPDPSW.2018.00164 – volume: 11 start-page: 546 issue: 4 year: 2016 ident: 1318_CR2 publication-title: Int J Sustain Dev Plan doi: 10.2495/SDP-V11-N4-546-557 – ident: 1318_CR19 doi: 10.1007/978-3-319-94180-6_24 – ident: 1318_CR55 – ident: 1318_CR60 doi: 10.1145/1375527.1375558 – ident: 1318_CR5 – volume: 22 start-page: 804 issue: 6 year: 2011 ident: 1318_CR14 publication-title: J Manuf Technol Manag doi: 10.1108/17410381111149657 – ident: 1318_CR63 doi: 10.1109/HPCC/SmartCity/DSS.2018.00116 – ident: 1318_CR64 – volume: 10 start-page: 723 issue: 4 year: 2015 ident: 1318_CR4 publication-title: J Real-Time Image Process doi: 10.1007/s11554-014-0442-x – ident: 1318_CR45 – ident: 1318_CR21 doi: 10.1007/978-3-319-94180-6_15 – volume-title: An efficient BDD-based implementation of Gauss-Seidel for CTMC analysis year: 2013 ident: 1318_CR41 – ident: 1318_CR47 doi: 10.1109/ICPADS.2011.91 – ident: 1318_CR26 doi: 10.1007/978-3-319-94180-6_13 – ident: 1318_CR38 – volume: 18 start-page: 246 issue: 12 year: 2018 ident: 1318_CR20 publication-title: IJCSNS - Int J Comput Sci Netw Secur – ident: 1318_CR61 doi: 10.1109/ICPPW.2014.30 – volume-title: Parallel iterative solution method for large sparse linear equation systems year: 2005 ident: 1318_CR33 – ident: 1318_CR54 – volume: 5 start-page: 20 issue: 4 year: 2016 ident: 1318_CR6 publication-title: J Sens Actuator Networks doi: 10.3390/jsan5040020 – start-page: 373 volume-title: Computational Analysis of Sound Scenes and Events year: 2018 ident: 1318_CR11 doi: 10.1007/978-3-319-63450-0_13 – ident: 1318_CR31 doi: 10.1007/978-3-319-94180-6_29 – ident: 1318_CR23 doi: 10.1007/978-3-319-01884-3_20 – volume: 9 start-page: 947 issue: 5 year: 2019 ident: 1318_CR30 publication-title: Appl Sci doi: 10.3390/app9050947 – volume: 122 start-page: 361 year: 2017 ident: 1318_CR1 publication-title: Energy Procedia doi: 10.1016/j.egypro.2017.07.286 – ident: 1318_CR40 – ident: 1318_CR65 – ident: 1318_CR22 doi: 10.1016/j.procs.2017.05.439 – ident: 1318_CR44 – volume: 64 start-page: 1107 year: 2015 ident: 1318_CR13 publication-title: Procedia Computer Science doi: 10.1016/j.procs.2015.08.566 – ident: 1318_CR24 doi: 10.1007/978-3-319-94180-6_16 – ident: 1318_CR59 doi: 10.1109/IPDPSW.2017.155 – volume: 34 start-page: 1518 issue: 5 year: 2011 ident: 1318_CR16 publication-title: J Netw Comput Appl doi: 10.1016/j.jnca.2010.08.002 – ident: 1318_CR9 doi: 10.1109/SGCF.2016.7492425 – volume: 5 start-page: 2615 year: 2017 ident: 1318_CR29 publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2668840 – ident: 1318_CR35 doi: 10.1016/j.procs.2017.05.439 – ident: 1318_CR51 doi: 10.1109/ICPADS.2016.0120 – ident: 1318_CR17 doi: 10.1109/ONDM.2008.4578420 – ident: 1318_CR32 doi: 10.1109/MASCOT.2004.1348189 – ident: 1318_CR15 doi: 10.1109/ISMS.2010.84 – ident: 1318_CR53 – volume: E101.D start-page: 2307 issue: 9 year: 2018 ident: 1318_CR57 publication-title: IEICE Trans Inf Syst doi: 10.1587/transinf.2017EDP7176 – volume: 76 start-page: 301 year: 2017 ident: 1318_CR3 publication-title: Futur Gener Comput Syst doi: 10.1016/j.future.2016.12.033 – volume-title: A Survey of Out-of-Core Analysis Techniques in Stochastic Modelling year: 2003 ident: 1318_CR42 – volume: 52 start-page: 56 issue: 10 year: 2009 ident: 1318_CR52 publication-title: Commun ACM doi: 10.1145/1562764.1562783 – volume: 2012 start-page: 1 year: 2012 ident: 1318_CR7 publication-title: ISRN Comput Graph doi: 10.5402/2012/728913 – ident: 1318_CR28 doi: 10.1007/978-3-319-94180-6 – ident: 1318_CR66 – ident: 1318_CR43 – ident: 1318_CR62 – ident: 1318_CR37 doi: 10.1007/978-3-319-94180-6_4 – ident: 1318_CR67 – volume: 53 start-page: 291 issue: 4 year: 2016 ident: 1318_CR8 publication-title: Int J Comput Appl Technol doi: 10.1504/IJCAT.2016.076790 – volume: 109 start-page: 1122 year: 2017 ident: 1318_CR27 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2017.05.440 – ident: 1318_CR48 doi: 10.1109/MuCoCoS.2013.6633600 – ident: 1318_CR25 doi: 10.1007/978-3-319-94180-6_30 – volume: 19 start-page: 171 issue: 2 year: 2014 ident: 1318_CR34 publication-title: Mob Networks Appl doi: 10.1007/s11036-013-0489-0 – ident: 1318_CR56 – volume: 13 start-page: 53 issue: 2 year: 2017 ident: 1318_CR10 publication-title: J Commun Softw Syst doi: 10.24138/jcomss.v13i2.373 – volume: 37 start-page: 75 issue: 1 year: 2017 ident: 1318_CR12 publication-title: Int J Oper Prod Manag doi: 10.1108/IJOPM-03-2015-0179 – volume: 13 start-page: 1 issue: 1 year: 2016 ident: 1318_CR58 publication-title: ACM Trans Archit Code Optim doi: 10.1145/2851500 – ident: 1318_CR46 |
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