EAGER: Identifying and Removing Barriers to Autovectorization

University Of California-Irvine, Irvine CA

Investigators

Abstract

Most modern microprocessors support some form of vector operations that allow the same operation to be applied to small vectors of arguments simultaneously. Studies have shown that use of these instructions can improve the performance of many scientific codes by a factor of 2 or more. Unfortunately, the state of the art in autovectorization falls far short of this goal, only achieving improvements of 20-30% on the same codes. While studies have shown that current autovectorizing compilers do not identify all of the opportunities for vectorization, little is known about why they fail to do so. The PIs plan to evaluate tradeoffs between different compiler optimizations and vectorization in an effort to understand how optimization choices affect opportunities for autovectorization. They will use an extensive set of benchmarks to evaluate these tradeoffs. This research will make it possible to develop better autovectorizing compilers by avoiding optimization choices that interfere with autovectorization. The performance benefits of such compilers will improve the performance of applications ranging from multimedia software to scientific computing.