Sökning: WFRF:(Brorsson Mats 1962 ) >
Regression-Based Pr...
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.
-
visa fler...
-
- Brorsson, Mats, 1962- (författare)
- KTH,Programvaruteknik och datorsystem, SCS,KTH Royal Institute of Technology, Sweden
-
visa färre...
-
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
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas