Scalable dynamic binary instrumentation for Blue Gene/L

Authors:
Martin Schulz;Dong Ahn;Andrew Bernat;Bronis R. de Supinski;Steven Y. Ko;Gregory Lee;Barry Rountree
Affiliations:
Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;University of Wisconsin, Madison, WI;Lawrence Livermore National Laboratory, Livermore, CA;University of Illinois, Urbana-Champaign, IL;University of California, San Diego, CA;University of Georgia, GA
Venue:
ACM SIGARCH Computer Architecture News - Special issue on the 2005 workshop on binary instrumentation and application
Year:
2005

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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.