Autonomic and Latency-Aware Degree of Parallelism Management in SPar Adriano Vogel 1 , Dalvan Griebler 1 , Daniele De Sensi 2 , Marco Danelutto 2 and Luiz Gustavo Fernandes 1 1 Pontifical Catholic University of Rio Grande do Sul (PUCRS) 2 Department of Computer Science, University of Pisa (UNIPI) 2018
Outline ● Introduction & Related Work ● SPar (overview) ● Parallelism & Latency ● Autonomic management of Parallelism Degree ● Experimental results ● Conclusions 2 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Stream processing applications 3 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
The scenario ● Challenges ○ Parallel Programming complexities ○ Productivity Programming and system architecture expertises ○ ● High-level parallel programming frameworks Intel Threading Building Blocks (TBB) ○ ○ FastFlow ○ StreamIt ● DSL (Domain-Specific Language) SPar ○ 4 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Related Work Work Library/System Environment Objective NORNIR Multi-core Manage throughput and power De Sensi et al. [4] consumption FastFlow Multi-core De Matteis et al. [5] Latency and energy efficiency SPL Multi-core Gedik et al. [2] High throughput without wasting computational resources FUGU Distributed Heinze et al. [6] Latency and the system utilization Multi-core Throughput Selva et al. [8] StreamIt This work SPar Multi-core Parallelism abstraction for latency 5 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
SPar: the concepts DSL for stream parallelism ● Internal DSL ● Fully C++ compliant (C++11 or higher) Exploits C++ attributes to expose stream parallelism In standard C++ (non parallel) code ● (à la OpenMP, somehow) Minimal set of attributes ● To identify stream sources and stream processors 6 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Overview of SPar [[ spar::ToStream ]] while ( true ){ item = read(); [[ spar::Stage,spar::Input( item ),spar::Output( item ),spar::Replicate( N ) ]]{ ID item = filter(item); AUX } [[ spar::Stage,spar::Input( item ) ]]{ write(item); } 7 } Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Overview of SPar [[ spar::ToStream ]] while ( true ){ item = read(); [[ spar::Stage,spar::Input( item ),spar::Output( item ),spar::Replicate( N ) ]]{ item = filter(item); } [[ spar::Stage,spar::Input( item ) ]]{ We want to avoid this … write(item); } 8 } Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Spar RunTime FastFlow ● Backend of SPar ● Provides necessary patterns and building blocks Rules Transform attributes into FastFlow building blocks ● Reusing business logic code from original seq C++ ● E.g. : [[spar::stage, … , spar::replicate(N)]] -> farm() 9 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Spar RunTime [[ spar::Stage,spar::Replicate( N ) ]] [[ spar::Stage ]] [[ spar::ToStream ]] 10 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Spar RunTime [[ spar::Stage,spar::Replicate( N ) ]] [[ spar::Stage ]] [[ spar::ToStream ]] Adaptivity needed 11 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
The Impact of Parallelism on Latency Lane Detection application running in a 8 cores - 16 SMT machine ● (from Griebler , D.; Hoffmann, R. B.; Danelutto, M.; Fernandes, L. G. “Higher -Level Parallelism Abstractions for Video Applications with SPar ”. In: 3 rd International Workshop on Reengineering for Parallelism in Heterogeneous Parallel Platforms, 2017) 12 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
The Impact of Parallelism on Latency 13 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Targets Goals: ● ○ Abstract definition of the parallelism degree in SPar ○ Latency monitoring Adapt the number of replicas on-the-fly ○ ● Contributions: An extension of the SPar DSL with a new parallelism abstraction for latency-sensitive ○ applications. ○ An experimental evaluation of the strategy’s effectiveness 14 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Autonomous Degree of Parallelism Implementation The solution ● Monitor observes and reports what’s going on ● Regulator applies pardegree regulation policies 15 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Autonomous Degree of Parallelism Implementation The regulator strategy ● 16 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Results: Threshold 10% and Latency constraint 180 ms Throughput (frames per second) and Latency of stream Number of Replicas used (Degree of Parallelism) items (ms). SF (scaling factor) replicas that activated or suspended in reconfigurations 17 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Results: Threshold 20% and Latency constraint 180 ms Throughput (frames per second) and Latency of stream Number of Replicas used (Degree of Parallelism) items (ms). SF (scaling factor) replicas that activated or suspended in reconfigurations 18 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Results: Threshold 10% and Latency constraint 200 ms Throughput (frames per second) and Latency of stream Number of Replicas used (Degree of Parallelism) items (ms). SF (scaling factor) replicas that activated or suspended in reconfigurations 19 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Results: Threshold 20% and Latency constraint 200 ms Throughput (frames per second) and Latency of stream Number of Replicas used (Degree of Parallelism) items (ms). SF (scaling factor) replicas that activated or suspended in reconfigurations 20 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Conclusion ● Latency is important ● SPar extended with a new parallelism abstraction ● On the fly adaptation of parallelism degree ● Effectiveness demonstrated with a stream processing application Future Work: Consider applications with a more complex structure ○ Evaluate approach in other latency sensitive applications ○ Proactive approaches ○ 21 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
References [1] Andrade, H.; Gedik, B.; Turaga, D. “Fundamentals of Stream Processing: Application Design, Systems, and Analytics”. Cambridge University Press, 2014. [2] Gedik, B.; Schneider, S.; Hirzel, M.; Wu, K.- L. “Elastic scaling for data stream processing”, IEEE Transactions on Parallel and Distributed Systems, vol. 25 – 6, 2014, pp. 1447 – 1463. [3]Su, Y.; Shi, F.; Talpur, S.; Wang, Y.; Hu, S.; Wei, J. “Achieving self - aware parallelism in stream programs”, Cluster Computing, vol. 18 – 2, 2015, pp. 949 – 962. [4] Sensi, D. D.; Torquati, M.; Danelutto, M. “A reconfiguration algorithm for power - aware parallel applications”, ACM Transactions on Architecture and Code Optimization (TACO), vol. 13 – 4, 2016, pp. 43. [5] De Matteis, T.; Mencagli, G. “Keep calm and react with foresight: strategies for low -latency and energy-efficient elastic data stream processing”. In: Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2016, pp. 13. [6] Heinze, T.; Pappalardo, V.; Jerzak, Z.; Fetzer, C. “Auto - scaling techniques for elastic data stream processing”. In: Data Engineering Workshops (ICDEW), 2014 IEEE 30th International Conference on, 2014, pp. 296 – 302. [7] Griebler, D. “Domain -Specific Language & Support Tool for High- Level Stream Parallelism”, Ph.D. Thesis, Faculdade de Informática - PPGCC - PUCRS, Porto Alegre, Brazil, 2016, 243p. [8] Selva, M.; Morel, L.; Marquet, K.; Frenot, S. “A monitoring system for runtime adaptations of streaming applications”. In: Parallel, Distributed and Network Based Processing (PDP), 2015 23rd Euromicro International Conference on, 2015, pp. 27 – 34. 22 Autonomic and Latency-Aware Degree of Parallelism Management in SPar
Thank you! E-mail: adriano.vogel@acad.pucrs.br Luiz Gustavo Adriano Vogel Dalvan Griebler Daniele De Sensi Marco Danelutto Fernandes
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