Entropy-based profile characterization and classification for automatic profile management

Jinpyo Kim, Wei Chung Hsu, Pen Chung Yew, Sreekumar R. Nair, Robert Y. Geva

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The recent adoption of pre-JIT compilation for the JVM and .NET platforms allows the exploitation of continuous profile collection and management at user sites. To support efficient pre-JIT type of compilation, this paper proposes and studies an entropy-based profile characterization and classification method. This paper first shows that highly accurate profiles can be obtained by merging a number of profiles collected over repeated executions with relatively low sampling frequency for the SPEC CPU2000 benchmarks. It also shows that simple characterization of the profile with information entropy can be used to guide sampling frequency of the profiler in an autonomous fashion. On the SPECjbb2000 benchmark, our adaptive profiler obtains a very accurate profile (94.5% similar to the baseline profile) with only 8.7% of the samples that would normally be collected using a IM instructions sampling interval. Furthermore, we show that entropy could also be used for classifying different program behaviors based on different input sets.

Original languageEnglish (US)
Title of host publicationAdvances in Computer Systems Architecture - 12th Asia-Pacific Conference, ACSAC 2007, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783540743088
StatePublished - 2007
Event12th Asia-Pacific Computer Systems Architecture Conference, ACSAC 2007 - Seoul, Korea, Republic of
Duration: Aug 23 2007Aug 25 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4697 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th Asia-Pacific Computer Systems Architecture Conference, ACSAC 2007
Country/TerritoryKorea, Republic of


Dive into the research topics of 'Entropy-based profile characterization and classification for automatic profile management'. Together they form a unique fingerprint.

Cite this