| Abstract |
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This paper proposes an algorithm to dynamically characterize
the coherency traffic occurring in DSM architectures.
This algorithm strongly relies on linear regressions
to isolate lines among the traffic. The main features are
a dynamic algorithm, robustness toward the noise and production
of fine characterizations of the traffic. At the end the
regularity is summarized in a set of regression lines found
and some statistics are provided. The driving idea is while
scientific code is widely considered as highly structured, a
precise quantification may expose the underlying regularity
due the code data structures.
We describe the algorithm step by step and give results
that show the relevance of the approach.
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 Additional Information
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Citation:
Jean-Thomas Acquaviva, Franck Quessette,
"Using Linear Regression to Characterize Data Coherency Traffic,"
mascots,
p. 26,
11th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS'03),
2003
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