Paper Info
Title
ARCS: Adaptive Runtime Configuration Selection for Power-Constrained OpenMP Applications
Abstract
Power is the most critical resource for the exascale high performance computing. In the future, system administrators might have to pay attention to the power consumption of the machine under different work loads. Hence, each application may have to run with an allocated power budget. Thus, achieving the best performance on future machines requires optimal performance subject to a power constraint. This additional performance requirement should not be the responsibility of HPC~(High Performance Computing) application developers. Optimizing the performance for a given power budget should be the responsibility of high-performance system software stack. Modern machines allow power capping of CPU and memory to implement power budgeting strategy. Finding the best runtime environment for a node at a given power level is important to get the best performance. This paper presents ARCS (Adaptive Runtime Configuration Selection) frameworkthat automatically selects the best runtime configuration for each OpenMPparallel region at a given power level. The framework uses OMPT (OpenMP Tools) API, APEX(Autonomic Performance Environment for eXascale), and Active Harmony frameworksto explore configuration search space and selects the best number of threads, scheduling policy, and chunk size for a given power level at run-time. We test ARCS using the NAS Parallel Benchmark, and proxy application LULESH with Intel Sandybridge, and IBM Power multi-core architectures. We show that for a given power level, efficient OpenMP runtime parameter selection can improve the execution time and energy consumption of an application up to 40% and 42% respectively.
Year
DOI
Venue
2016
10.1109/CLUSTER.2016.39
2016 IEEE International Conference on Cluster Computing (CLUSTER)
Keywords
Field
DocType
OpenMP,Runtime Parameter Selection,Runtime Adaptation,Power Aware Computing,Power Constrained System
Power budget,System software,IBM,Supercomputer,Scheduling (computing),Computer science,Parallel computing,Thread (computing),Real-time computing,Execution time,Benchmark (computing),Distributed computing
Conference
ISSN
ISBN
Citations 
1552-5244
978-1-5090-3654-7
0
PageRank 
References 
Authors
0.34
14
7
Name
Order
Citations
PageRank
Md Abdullah Shahneous Bari100.34
Nicholas Chaimov2263.55
Abid Muslim Malik3124.12
Kevin A. Huck411914.53
Barbara M. Chapman5904119.20
Allen D. Malony61787190.85
Osman Sarood720110.14