Paper Info
Title
TOEP: Threshold Oriented Energy Prediction Mechanism for MPI-OpenMP Hybrid Applications
Abstract
Evaluating the execution time and energy consumption of parallel programs is a primary research topic for many HPC environments. Whereas much work has been done to evaluate the non-functional behavior for single parallel programming models such as MPI or OpenMP, little work exists for hybrid programming models such as MPI/OpenMP. This paper proposes the Threshold Oriented Energy Prediction (TOEP) approach which uses the Random Forest Modeling (RFM) to train models for execution time and energy consumption of hybrid MPI/OpenMP programs. Training data (performance measurements) are reduced by ignoring code regions that have little impact on the overall energy consumption and runtime of a program and also based on the variable importance parameter of RFM. A selection parameter is introduced that selects a trade-off solution between the number of modeling points (measurement or training data) required and prediction accuracy. An exploratory study on the proposed prediction approach was employed for a few candidate hybrid applications namely HOMB, CoMD, and AMG2006-Laplace. The experimental results manifested the energy prediction accuracy of over 86.17% for large performance datasets of the candidate applications at a reduced computational effort of less than 17 seconds.
Year
DOI
Venue
2018
10.1109/IC3.2018.8530575
2018 Eleventh International Conference on Contemporary Computing (IC3)
Keywords
Field
DocType
Energy Prediction,HPC,Hybrid,Scientific Applications
Training set,Data modeling,Pattern recognition,Computer science,Parallel computing,Sampling (statistics),Execution time,Artificial intelligence,Random forest,Energy consumption,Exploratory research,Hybrid programming
Conference
ISSN
ISBN
Citations 
2572-6110
978-1-5386-6836-8
0
PageRank 
References 
Authors
0.34
7
3
Name
Order
Citations
PageRank
shajulin benedict16913.68
Philipp Gschwandtner2587.15
Thomas Fahringer32847254.09