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Thesis Defense - Seidl

ECOT 831

Increasing the Reference Locality of Dynamically Allocated Objects
Matthew L. Seidl
Computer Science PhD Candidate

Heap-allocated objects play an important role in many modern programs. Our investigations of dynamically allocated objects show that there is a strong skew in object reference behavior: a small percentage of the objects receive a large percentage of the references. In this thesis, we describe Avon, a memory allocation system that uses the skew in reference density between objects to improve program performance.

The Avon system is designed to increase the reference density of large programs that make heavy use of dynamically allocated memory by segregating objects into separate heap areas based on their reference behavior and lifetime. We show how it is possible to predict object reference behavior at allocation time. Our system does this by using profile data to observe both the information available to the allocator when it creates an object and the resulting behavior of that object. We also show how to build an allocator that can efficiently gather the allocation time information necessary to predict object behavior. As a demonstration of the Avon system, we demonstrate the effectiveness of this approach by evaluating a prototype of the system on an a number of memory intensive programs.

Committee: Benjamin Zorn, Associate Professor (Chair)
Amer Diwan, Assistant Professor
Dirk Grunwald, Associate Professor
David Detlefs, Sun Microsystems
William Waite, Professor

See also:
Department of Computer Science
College of Engineering and Applied Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
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