Monday, April 28, 2008
9:30 AM
Computer Studies Bldg. Room 632
Ph.D. Thesis Proposal
Tongxin Bai
University of Rochester
Blended Analysis for Speculative Specialization and Coarse-Grained Redundancy Detection
Program specialization finds additional optimization opportunities by
targeting some rather than all executions of a program. The narrower
input domain increases the the number of runtime invariants and hence
exposes more runtime redundancies. Program specialization improves
program performance by eliminating the runtime redundancies. However,
the types of invariants that traditional specialization methods can
make use of are extremely limited, typically scalar variables. I
propose a set of new techniques, spanning from compiler and profile
analysis to specialization transformation, to extend the capability of
program specialization so that it can utilize the invariance hidden in
complex data structures. In the second half of the proposal I intend
to measure the similarity between runtime function instances. With
this information the analysis helps programmers understand program
performance issues in terms of computation redundancies. Besides the
redundancy issues memory bandwidth bottleneck is another performance
concern, particularly in this multi-core era. We propose a technique
called {\it partial caching of working sets} (PAW) to reduce cache
misses in a shared-cache environment through intentionally pinning
down a small part of each large working set in the cache.