By Topic

Evaluating Benchmark Subsetting Approaches

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.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Yi, J.J. ; Networking & Comput. Syst. Group, Freescale Semicond., Inc., Austin, TX ; Sendag, R. ; Eeckhout, L. ; Joshi, A.
more authors

To reduce the simulation time to a tractable amount or due to compilation (or other related) problems, computer architects often simulate only a subset of the benchmarks in a benchmark suite. However, if the architect chooses a subset of benchmarks that is not representative, the subsequent simulation results will, at best, be misleading or, at worst, yield incorrect conclusions. To address this problem, computer architects have recently proposed several statistically-based approaches to subset a benchmark suite. While some of these approaches are well-grounded statistically, what has not yet been thoroughly evaluated is the: 1) absolute accuracy; 2) relative accuracy across a range of processor and memory subsystem enhancements; and 3) representativeness and coverage of each approach for a range of subset sizes. Specifically, this paper evaluates statistically-based subsetting approaches based on principal components analysis (PCA) and the Plackett and Burman (P&B) design, in addition to prevailing approaches such as integer vs. floating-point, core vs. memory-bound, by language, and at random. Our results show that the two statistically-based approaches, PCA and P&B, have the best absolute and relative accuracy for CPI and energy-delay product (EDP), produce subsets that are the most representative, and choose benchmark and input set pairs that are most well-distributed across the benchmark space. To achieve a 5% absolute CPI and EDP error, across a wide range of configurations, PCA and P&B typically need about 17 benchmark and input set pairs, while the other five approaches often choose more than 30 benchmark and input set pairs

Published in:

Workload Characterization, 2006 IEEE International Symposium on

Date of Conference:

25-27 Oct. 2006