NDLI: Reducing overheads for acquiring dynamic memory traces

Content Provider	IEEE Xplore Digital Library
Author	Xiaofeng Gao Laurenzano, M. Simon, B. Snavely, A.
Copyright Year	2005
Description	Author affiliation: San Diego Supercomput. Center, La Jolla, CA, USA (Xiaofeng Gao; Laurenzano, M.; Simon, B.; Snavely, A.)
Abstract	Tools for acquiring dynamic memory address information for large scale applications are important for performance modeling, optimization, and for trace-driven simulation. However, straightforward use of binary instrumentation tools for such a fine-grained task as address tracing can cause astonishing slowdown in application run time. For example, in a large scale FY05 collaboration with the Department of Defense High Performance Computing Modernization Office (HPCMO), over 1 million processor hours were expended in order to gather address information on 7 parallel applications. In this paper, we discuss in detail the issues surrounding the performance of memory address acquisition using low-level binary instrumentation tracing. We present three techniques and optimizations to improve performance: 1) SimPoint-guided sampling, 2) instrumentation tool routine optimization, and 3) reduction of instrumentation points through static application analysis. The use of these three techniques together reduces instrumented application slowdown by an order of magnitude. The techniques are generally applicable and have been deployed in the MetaSim tracer thereby enabling memory address acquisition for real-sized applications. We expect the optimizations reported here reduce the HPCMO effort by approximately 80% in FY06.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Comput. Archit
Starting Page	46
Ending Page	55
File Size	358388
Page Count	10
File Format	PDF
ISBN	0780394615
DOI	10.1109/IISWC.2005.1526000
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Instruments Supercomputers Large-scale systems High performance computing Performance analysis Atomic measurements Computational modeling Collaborative work Sampling methods Costs
Content Type	Text
Resource Type	Article

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