Paper Info
Title
Dynamic binary instrumentation and data aggregation on large scale systems
Abstract
Dynamic binary instrumentation for performance analysis on large scale architectures such as the IBM Blue Gene/L system (BG/L) poses unique challenges. Their unprecedented scale and often limited OS support require new mechanisms to organize binary instrumentation, to interact with the target application, and to collect the resulting data. We describe the design and current status of a new implementation of the Dynamic Probe Class Library (DPCL) API for large scale systems. DPCL provides an easy to use layer for dynamic instrumentation on parallel MPI applications based on the DynInst dynamic instrumentation library for sequential platforms. Our work includes modifying DynInst to control instrumentation from remote I/O nodes and porting DPCL's communication for performance data collection to use MRNet, a tree-based overlay network that (TBON) supports scalable multicast and data reduction. We describe extensions to the DPCL API that support instrumentation of task subsets and aggregation of collected performance data.
Year
DOI
Venue
2007
10.1007/s10766-007-0036-3
International Journal of Parallel Programming
Keywords
Field
DocType
performance analysis tools.,dynamic instrumentation,scal- able data collection,data reduction,binary instrumentation,dyninst dynamic instrumentation library,support instrumentation,dpcl api,dynamic binary instrumentation,performance data,porting dpcl,massively parallel architectures,data aggregation,performance data collection,large scale system,data collection,overlay network
Data collection,IBM,Computer science,Parallel computing,L-system,Porting,Multicast,Data aggregator,Overlay network,Scalability
Journal
Volume
Issue
ISSN
35
3
1573-7640
Citations 
PageRank 
References 
3
0.39
7
Authors
7
Name
Order
Citations
PageRank
Gregory L. Lee119914.30
Martin Schulz22227129.64
Dong H. Ahn332522.61
Andrew Bernat4111.02
Bronis R. de Supinskil530.39
Steven Y. Ko647145.08
Barry Rountree7101351.24