| Scalable dynamic binary instrumentation for Blue Gene/L |
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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
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Authors
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Martin Schulz
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Lawrence Livermore National Laboratory, Livermore, CA
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Dong Ahn
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Lawrence Livermore National Laboratory, Livermore, CA
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Andrew Bernat
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University of Wisconsin, Madison, WI
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Bronis R. de Supinski
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Lawrence Livermore National Laboratory, Livermore, CA
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Steven Y. Ko
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University of Illinois, Urbana-Champaign, IL
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Gregory Lee
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University of California, San Diego, CA
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Barry Rountree
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University of Georgia, GA
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Downloads (6 Weeks): 0, Downloads (12 Months): 18, Citation Count: 2
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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.
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[doi> 10.1109/2.471178]
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CITED BY 2
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Bronis R. De Supinski , Martin Schulz , Vasily V. Bulatov , William Cabot , Bor Chan , Andrew W. Cook , Erik W. Draeger , James N. Glosli , Jeffrey A. Greenough , Keith Henderson , Alison Kubota , Steve Louis , Brian J. Miller , Mehul V. Patel , Thomas E. Spelce , Frederick H. Streitz , Peter L. Williams , Robert K. Yates , Andy Yoo , George Almasi , Gyan Bhanot , Alan Gara , John A. Gunnels , Manish Gupta , Jose Moreira , James Sexton , Bob Walkup , Charles Archer , Francois Gygi , Timothy C. Germann , Kai Kadau , Peter S. Lomdahl , Charles Rendleman , Michael L. Welcome , William Mclendon , Bruce Hendrickson , Franz Franchetti , Stefan Kral , Jürgen Lorenz , Christoph W. Überhuber , Edmond Chow , Ümit Çatalyürek, BlueGene/L applications: Parallelism On a Massive Scale, International Journal of High Performance Computing Applications, v.22 n.1, p.33-51, February 2008
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Gregory L. Lee , Dong H. Ahn , Dorian C. Arnold , Bronis R. de Supinski , Matthew Legendre , Barton P. Miller , Martin Schulz , Ben Liblit, Lessons learned at 208K: towards debugging millions of cores, Proceedings of the 2008 ACM/IEEE conference on Supercomputing, November 15-21, 2008, Austin, Texas
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