By Topic

The stack growth function: cache line reference models

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
$33 $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

2 Author(s)
M. Kobayashi ; Amdahl Corp., Sunnyvale, CA, USA ; M. H. MacDougall

To model cache behavior in a multiprogramming environment, it is necessary to know the number of distinct lines referenced in an execution interval. The stack growth function (SGF) relates the mean number of references (or instructions) to the number of distinct lines referenced; it can be viewed as the inverse function of the mean working set size. A fast, one-pass algorithm to compute the SGF for a given referenced string is presented. SGFs measured for some 40 real programs show that a simple exponential model fits the SGFs reasonably well over a range of execution intervals. Parameters of the inverse exponential model are presented for several program mixes and cache line sizes of 32 and 64 bytes. Separate instruction and data SGFs also are examined, and execution interval distribution effects are considered

Published in:

IEEE Transactions on Computers  (Volume:38 ,  Issue: 6 )