Paper Info
Title
General Hybrid Parallel Profiling
Abstract
A hybrid parallel measurement system offers the potential to fuse the principal advantages of probe-based tools, with their exact measures of performance and ability to capture event semantics, and sampling-based tools, with their ability to observe performance detail with less overhead. Creating a hybrid profiling solution is challenging because it requires new mechanisms for integrating probe and sample measurements and calculating profile statistics during execution. In this paper, we describe a general hybrid parallel profiling tool that has been implemented in the TAU Performance System. Its generality comes from the fact that all of the features of the individual methods are retained and can be flexibly controlled when combined to address the measurement requirements for a particular parallel application. The design of the hybrid profiling approach is described and the implementation of the prototype in TAU presented. We demonstrate hybrid profiling functionality first on a simple sequential program and then show its use for several OpenMP parallel codes from the NAS Parallel Benchmark. These experiments also highlight the improvements in overhead efficiency made possible by hybrid profiling. A large-scale ocean modeling code based on OpenMP and MPI, MPAS-Ocean, is used to show how the TAU hybrid profiling tool can be effective at exposing performance-limiting behavior that would be difficult to identify otherwise.
Year
DOI
Venue
2014
10.1109/PDP.2014.38
Parallel, Distributed and Network-Based Processing
Keywords
Field
DocType
program diagnostics,sampling methods,software performance evaluation,MPAS-Ocean,MPI,NAS parallel benchmark,OpenMP parallel codes,TAU hybrid profiling tool,TAU performance system,general hybrid parallel profiling,hybrid parallel measurement system,large-scale ocean modeling code,probe-based tool,sampling-based tool,Parallel,analysis,performance,tools
System of measurement,Profiling (computer programming),Computer science,Parallel computing,Ocean modeling,Sampling (statistics),Fuse (electrical),Semantics,Generality,Distributed computing
Conference
ISSN
Citations 
PageRank 
1066-6192
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Allen D. Malony125131.26
Kevin A. Huck211914.53