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Regression-Based Prediction for Task-Based Program Performance
Izmir Inst Technol, Comp Engn Dept, TR-35430 Gulbahce, Urla Izmir, Turkey..
Informat Technol Univ, Lahore 54000, Punjab, Pakistan..
SICS Swedish ICT AB, SE-16429 Stockholm, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.ORCID iD: 0000-0002-9637-2065
2019 (English)In: Journal of Circuits, Systems and Computers, ISSN 0218-1266, Vol. 28, no 4, article id 1950060Article in journal (Refereed) Published
Abstract [en]

As multicore systems evolve by increasing the number of parallel execution units, parallel programming models have been released to exploit parallelism in the applications. Task-based programming model uses task abstractions to specify parallel tasks and schedules tasks onto processors at runtime. In order to increase the efficiency and get the highest performance, it is required to identify which runtime configuration is needed and how processor cores must be shared among tasks. Exploring design space for all possible scheduling and runtime options, especially for large input data, becomes infeasible and requires statistical modeling. Regression-based modeling determines the effects of multiple factors on a response variable, and makes predictions based on statistical analysis. In this work, we propose a regression-based modeling approach to predict the task-based program performance for different scheduling parameters with variable data size. We execute a set of task-based programs by varying the runtime parameters, and conduct a systematic measurement for influencing factors on execution time. Our approach uses executions with different configurations for a set of input data, and derives different regression models to predict execution time for larger input data. Our results show that regression models provide accurate predictions for validation inputs with mean error rate as low as 6.3%, and 14% on average among four task-based programs.

Place, publisher, year, edition, pages
WORLD SCIENTIFIC PUBL CO PTE LTD , 2019. Vol. 28, no 4, article id 1950060
Keywords [en]
Performance prediction, task-based programs, regression
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-249799DOI: 10.1142/S0218126619500609ISI: 000462969800009Scopus ID: 2-s2.0-85049081368OAI: oai:DiVA.org:kth-249799DiVA, id: diva2:1306591
Note

QC 20190424

Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-04-24Bibliographically approved

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Brorsson, Mats

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