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Regression-Based Prediction for Task-Based Program Performance

Oz, Isil (författare)
Izmir Institute of Technology, Turkey,Izmir Inst Technol, Comp Engn Dept, TR-35430 Gulbahce, Urla Izmir, Turkey.
Bhatti, Mohammad. K. (författare)
Information Technology University, India,Informat Technol Univ, Lahore 54000, Punjab, Pakistan.
Popov, Konstantin (författare)
RISE,SICS,SICS Swedish ICT AB, SE-16429 Stockholm, Sweden.
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Brorsson, Mats, 1962- (författare)
KTH,Programvaruteknik och datorsystem, SCS,KTH Royal Institute of Technology, Sweden
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Izmir Institute of Technology, Turkey Izmir Inst Technol, Comp Engn Dept, TR-35430 Gulbahce, Urla Izmir, Turkey (creator_code:org_t)
WORLD SCIENTIFIC PUBL CO PTE LTD, 2019
2019
Engelska.
Ingår i: Journal of Circuits, Systems and Computers. - : WORLD SCIENTIFIC PUBL CO PTE LTD. - 0218-1266. ; 8:4
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

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

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