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Computer Science @ Rochester
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.