NDLI: Matching Application Signatures for Performance Predictions Using a Single Execution

Content Provider	IEEE Xplore Digital Library
Author	Jayakumar, A. Murali, P. Vadhiyar, S.
Copyright Year	2015
Description	Author affiliation: Supercomput. Educ. & Res. Centre, Indian Inst. of Sci., Bangalore, India (Jayakumar, A.; Murali, P.; Vadhiyar, S.)
Abstract	Performance predictions for large problem sizes and processors using limited small scale runs are useful for a variety of purposes including scalability projections, and help in minimizing the time taken for constructing training data for building performance models. In this paper, we present a prediction framework that matches execution signatures for performance predictions of HPC applications using a single small scale application execution. Our framework extracts execution signatures of applications and performs automatic phase identification of different application phases. Application signatures of the different phases are matched with the execution profiles of reference kernels stored in a kernel database. The performance of the reference kernels are then used to predict the performance of the application phases. For phases that do not match significantly, our framework performs static analysis of loops and functions in the application to provide prediction ranges. We demonstrate this integrated set of techniques in our framework with three large scale applications, including GTC, a Particle-in-Cell code for turbulence simulation, Sweep3d, a 3D neutron transport application and SMG2000, a multigrid solver. We show that our prediction ranges are accurate in most cases.
Starting Page	1161
Ending Page	1170
File Size	1400366
Page Count	10
File Format	PDF
ISBN	9781479986491
ISSN	15302075
DOI	10.1109/IPDPS.2015.20
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-25
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Program processors Benchmark testing Predictive models Histograms Training Databases Phase Identification Modeling Prediction Matching Application Signatures Kernels
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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