ACM Home Page
Please provide us with feedback. Feedback
Scalable dynamic binary instrumentation for Blue Gene/L
Full text PdfPdf (298 KB)
Source ACM SIGARCH Computer Architecture News archive
Volume 33 ,  Issue 5  (December 2005) table of contents
Special issue on the 2005 workshop on binary instrumentation and application
SPECIAL ISSUE: WBIA'05 table of contents
Pages: 9 - 14  
Year of Publication: 2005
ISSN:0163-5964
Authors
Martin Schulz  Lawrence Livermore National Laboratory, Livermore, CA
Dong Ahn  Lawrence Livermore National Laboratory, Livermore, CA
Andrew Bernat  University of Wisconsin, Madison, WI
Bronis R. de Supinski  Lawrence Livermore National Laboratory, Livermore, CA
Steven Y. Ko  University of Illinois, Urbana-Champaign, IL
Gregory Lee  University of California, San Diego, CA
Barry Rountree  University of Georgia, GA
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 0,   Downloads (12 Months): 18,   Citation Count: 2
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1127577.1127581
What is a DOI?

ABSTRACT

Dynamic binary instrumentation for performance analysis on new, large scale architectures such as the IBM Blue Gene/L system (BG/L) poses new challenges. Their scale---with potentially hundreds of thousands of compute nodes---requires new, more scalable mechanisms to deploy and to organize binary instrumentation and to collect the resulting data gathered by the inserted probes. Further, many of these new machines don't support full operating systems on the compute nodes; rather, they rely on light-weight custom compute kernels that do not support daemon-based implementations.We describe the design and current status of a new implementation of the DPCL (Dynamic Probe Class Library) API for BG/L. DPCL provides an easy to use layer for dynamic instrumentation on parallel MPI applications based on the DynInst dynamic instrumentation mechanism for sequential platforms. Our work includes modifying DynInst to control instrumentation from remote I/O nodes and porting DPCL's communication to use MRNet, a scalable data reduction network for collecting performance data. We describe extensions to the DPCL API that support instrumentation of task subsets and aggregation of collected performance data. Overall, our implementation provides a scalable infrastructure that provides efficient binary instrumentation on BG/L.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
MPI Debugging Interface. http://www-unix.mcs.anl.gov/mpi/mpi-debug/, Sept. 2005.
 
2
SLURM: Simple Linux Utility for Resource Management. http://www.llnl.gov/linux/slurm/, June 2005.
 
3
 
4
 
5
J. DelSignore. TotalView on Blue Gene/L. Presented at "Blue Gene/L: Applications, Architecture and Software Workshop", presentation available at http://www.llnl.gov/asci/platforms/bluegene/papers/26delsignore.pdf.
 
6
 
7
IBM. An Overview of the BlueGene/L Supercomputer. Whitepaper available at http://www-fp.mcs.anl.gov/bgconsortium.
 
8
T. Ludwig, R. Wismüller, V. Sunderam, and A. Bode. OMIS --- On-line Monitoring Interface Specification (Version 2.0), volume 9 of LRR-TUM Research Report Series. Shaker Verlag, Aachen, Germany, 1997. ISBN 3-8265-3035-7.
 
9
J. May and J. Gyllenhaal. Tool Gear: Infrastructure for Parallel Tools. In Proceedings of the 2003 International Conference on Parallel and Distributed Techniques and Applications, June 2003.
 
10
 
11


Collaborative Colleagues:
Martin Schulz: colleagues
Dong Ahn: colleagues
Andrew Bernat: colleagues
Bronis R. de Supinski: colleagues
Steven Y. Ko: colleagues
Gregory Lee: colleagues
Barry Rountree: colleagues