Application Signature: a new way to predict application performance

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Todi, Rajat
Major Professor
John Gustafson
Gurpur Prabhu
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Computer Science

Computer Science—the theory, representation, processing, communication and use of information—is fundamentally transforming every aspect of human endeavor. The Department of Computer Science at Iowa State University advances computational and information sciences through; 1. educational and research programs within and beyond the university; 2. active engagement to help define national and international research, and 3. educational agendas, and sustained commitment to graduating leaders for academia, industry and government.

The Computer Science Department was officially established in 1969, with Robert Stewart serving as the founding Department Chair. Faculty were composed of joint appointments with Mathematics, Statistics, and Electrical Engineering. In 1969, the building which now houses the Computer Science department, then simply called the Computer Science building, was completed. Later it was named Atanasoff Hall. Throughout the 1980s to present, the department expanded and developed its teaching and research agendas to cover many areas of computing.

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Computer Science

Advances in digital computers have been spectacular but increasingly complex to model. Even the cycle-accurate simulators, which are costly to develop and run have questionable accuracy. This thesis provides a simple, accurate, scientifically proven, and analytic model to accurately predict the performance of real applications. The method creates two profiles as a function of time or problem sizes. The first profile, Hardware Signature, that reveals computer hardware speed, is obtained by running a universal benchmark, HINT or by running an analytical model, AHINT. The second profile, Application Signature (APPMAP), that divulges intrinsic application requirements, can be obtained by four different methods outlined in the thesis. The convolution of these two profiles are used to predict real application performance. The model was tested using 25000 performance measurements and was validated by determining Pearson's correlation, Spearman's rank correlation and maximum deviation from linearity. Furthermore, through the Hardware Signature of the analytical models, one can obtain precise answers to questions about optimum size of memory, caches, and the numerical precision for a given clock rate.

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Wed Jan 01 00:00:00 UTC 2003