David I. August
Professor in the Department of Computer Science, Princeton University
Affiliated with the Department of Electrical Engineering, Princeton University
Ph.D. May 2000, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

Office: Computer Science Building Room 221
Email: august@princeton.edu
Phone: (609) 258-2085
Fax: (609) 964-1699
Administrative Assistant: Pamela DelOrefice, (609) 258-5551

Front Page Publication List (with stats) Curriculum Vitae (PDF) The Liberty Research Group

Publications

Rapid Development of Flexible Validated Processor Models [abstract] (PDF)
David A. Penry, Manish Vachharajani, and David I. August
Liberty Research Group Technical Report 04-03, November 2004.

For a variety of reasons, most architectural evaluations use simulation models. An accurate baseline model validated against existing hardware provides confidence in the results of these evaluations. Meanwhile, a meaningful exploration of the design space requires a wide range of quickly-obtainable variations of the baseline. Unfortunately, these two goals are generally considered to be at odds; the set of validated models is considered exclusive of the set of easily malleable models. Vachharajani et al. challenge this belief and propose a modeling methodology they claim allows rapid construction of flexible validated models. Unfortunately, they only present anecdotal and secondary evidence to support their claims.

In this paper, we present our experience using this methodology to construct a validated flexible model of Intel's Itanium 2 processor. Our practical experience lends support to the above claims. Our initial model was constructed by a single researcher in only 11 weeks and predicts processor cycles-per-instruction (CPI) to within 7.9% on average for the entire SPEC CINT2000 benchmark suite. Our experience with this model showed us that aggregate accuracy for a metric like CPI is not sufficient. Aggregate measures like CPI may conceal remaining internal ``offsetting errors'' which can adversely affect conclusions drawn from the model. Using this as our motivation, we explore the flexibility of the model by modifying it to target specific error constituents, such as front-end stall errors. In 2 1/2 person-weeks, average CPI error was reduced to 5.4%. The targeted error constituents were reduced more dramatically; front-end stall errors were reduced from 5.6% to 1.6%. The swift implementation of significant new architectural features on this model further demonstrated its flexibility.