Paper Info
Title
Scale-Ea: A Scalability Aware Performance Tuning Framework For Openmp Applications
Abstract
HPC application developers, including OpenMP-based application developers, have stepped forward to endeavor the future design trends of exa-scale machines, such as, increased number of threads/cores, heterogeneous architectures, multiple levels of memories, and so forth; and, they have initiated procedures to address application level challenges, such as, data-driven scalability issues, energy consumption requirements, data availability needs, and so forth. Despite the existence of manual performance tuning solutions, users still deem it to be an intricate process. This paper proposes a scalability aware autotuning framework (SCALE-EA) that automatically identifies an efficient number of threads for OpenMP parallel regions using a Firefly Algorithm (FA) and a newly designed Modeling Assisted Firefly Algorithm (MAFA). MAFA of SCALE-EA was implemented in two approaches: Modeling Assisted Firefly Algorithm with Random Forest Modeling support (MAFA-RFM) and Modeling Assisted Firefly Algorithm with Linear Regression Modeling support (MAFA-LRM). The modeling and prediction algorithms of the proposed MAFA of SCALE-EA were based on the execution time and the hardware performance events of code regions of OpenMP applications. Experiments were conducted on two machines, namely, a Haswell based machine and an AMD Opteron based 48 core machine. The experimental results of the MAFA of SCALE-EA manifested the energy efficiencies of 31.21 to 77.3 percentage and the search time efficiencies of 5.53 to 32.56 percentage for candidate OpenMP applications such as CoMD, Arraybench, Taskbench, and Syncbench.
Year
DOI
Venue
2018
10.12694/scpe.v19i1.1390
SCALABLE COMPUTING-PRACTICE AND EXPERIENCE
Keywords
Field
DocType
Auto Tuning, OpenMP Applications, Modeling and Scalability
Data availability,Computer science,Parallel computing,Thread (computing),Firefly algorithm,Execution time,Random forest,Performance tuning,Energy consumption,Scalability
Journal
Volume
Issue
ISSN
19
1
1895-1767
Citations 
PageRank 
References 
0
0.34
0
Authors
1
Name
Order
Citations
PageRank
shajulin benedict16913.68