Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Evaluating the efficacy of statistical simulation for design space exploration

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
6 Author(s)
Joshi, A. ; Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA ; Yi, J.J. ; Bell, R.H., Jr. ; Eeckhout, L.
more authors

Recent research has proposed statistical simulation as a technique for fast performance evaluation of superscalar microprocessors. The idea in statistical simulation is to measure a program's key performance characteristics, generate a synthetic trace with these characteristics, and simulate the synthetic trace. Due to the probabilistic nature of statistical simulation the performance estimate quickly converges to a solution, making it an attractive technique to efficiently cull a large microprocessor design space. In this paper, we evaluate the efficacy of statistical simulation in exploring the design space. Specifically, we characterize the following aspects of statistical simulation: (i) fidelity of performance bottlenecks, with respect to cycle-accurate simulation of the program, (ii) ability' to track design changes, and (Hi) trade-off between accuracy and complexity in statistical simulation models. In our characterization experiments, we use the Plackett & Burman (P&B) design to systematically stress statistical simulation by creating different performance bottlenecks. The key results from this paper are: (1) Synthetic traces stress at least the same 10 most significant processor performance bottlenecks as the original workload, (2) Statistical simulation can effectively track design changes to identify feasible design points in a large design space of aggressive microarchitectures, (3) Our evaluation of 4 statistical simulation models shows that although a very detailed model is needed to achieve a good absolute accuracy in performance estimation, a simple model is sufficient to achieve good relative accuracy, and (4) The P&B design technique can be used to quickly identify areas to focus on to improve the accuracy of the statistical simulation model.

Published in:

Performance Analysis of Systems and Software, 2006 IEEE International Symposium on

Date of Conference:

19-21 March 2006