Phase-Aware Program Profiling

August 24, 2004
Chandra Krintz | University of California, Santa Barbara

The ever increasing network bandwidth and availability has made the vision of ubiquitous computing a reality: Users can access the Internet’s vast offerings anytime and anywhere. Moreover, battery-powered devices such as personal digital assistants and web-enabled mobile phones have successfully emerged as new access points to the world’s digital infrastructure. This ubiquity offers a new opportunity for software developers: users can now participate in the software development, optimization, and evolution process while they use their software.

Such participation requires effective techniques for gathering profile information from remote, resource constrained devices. Further, these techniques must be unobtrusive and transparent to the user. Hence, profiles must be gathered using minimal computation, communication, and power. To this end, we present a flexible hardware-software scheme that will enable embedded remote profiling. We rely on the extraction of meta information from executing programs in the form of phases, and then use this information to guide intelligent online sampling and to manage the communication of those samples. Our results indicate that phase-based remote profiling can reduce the communication, computation, and energy consumption overheads by 50-75% over random and periodic sampling.

Speaker Details

Chandra Krintz is an Assistant Professor at the University of California, Santa Barbara (UCSB). She joined the UCSB faculty in 2001 after receiving her Ph.D. in Computer Science from the University of California, San Diego (UCSD) under the advisement of Dr. Brad Calder. Chandra also received her M.S. degree in Computer Science from UCSD. Chandra’s research interests include adaptive compiler and runtime techniques that improve program performance or reduce power consumption by exploiting the time-varying behavior in underlying resource performance and program execution.