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Regression-Based Prediction for Task-Based Program Performance
Izmir Institute of Technology, Turkey.
Information Technology University, India.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0002-9431-5139
KTH Royal Institute of Technology, Sweden.
2019 (English)In: Journal of Circuits, Systems and Computers, ISSN 0218-1266, Vol. 8, 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
2019. Vol. 8, no 4, article id 1950060
Keywords [en]
Performance prediction, regression, task-based programs, Computer systems programming, Forecasting, Input output programs, Parallel processing systems, Parallel programming, Regression analysis, Scheduling, Parallel programming model, Regression-based model, Run-time configuration, Scheduling parameters, Task-based, Task-based programming, Multicore programming
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-34593DOI: 10.1142/S0218126619500609Scopus ID: 2-s2.0-85049081368OAI: oai:DiVA.org:ri-34593DiVA, id: diva2:1238758
Available from: 2018-08-14 Created: 2018-08-14 Last updated: 2019-07-01Bibliographically approved

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Popov, Konstantin

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