Inefficiencies abound in complex, layered software. A variety ofinefficiencies show up as wasteful memory operations, such as redundantor useless memory loads and stores. Aliasing, limited optimization scopes,and insensitivity to input and execution contexts act as severe deterrentsto staticprogram analysis. Microscopic observation of whole executions atinstruction- and operand-levelgranularity breaks down abstractions andhelps recognize redundancies that masqueradein complex programs. Inthis talk, I will describe various wasteful memory operations, whichpervasively exist in modern software packages and expose great potential for optimization. I will discuss the design of a fine-grained instrumentationbased profiling framework that identifies wasteful operations in their contexts, which guides nontrivial performance improvement. Furthermore, I will show our recent improvement to the profiling framework by abandoning instrumentation, which reduces the runtime overhead from 10x to 3% on average.
Xu Liu is an assistant professor in the Department of Computer Science at College of William and Mary. He obtained his Ph.D. from Rice University in 2014 and joined William and Mary in the same year. Prof. Liu works on several open-source profiling tools, which are world-widely used at universities, DOE national laboratories, and in industry. Prof. Liu has published a number of papers in high-quality venues. His papers received Best Paper Award at SC'15, PPoPP'18, PPoPP'19, and ASPLOS'17 Highlights, and Distinguished Paper Award at ICSE'19.
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