Parallelizing Compiler Techniques Based on Linear Inequalities

Shared-memory multiprocessors, built out of the latest microprocessors, are becoming a widely available class of computationally powerful machines. These affordable multiprocessors can potentially deliver supercomputer-like performance to the general public. To effectively harness the power of these machines it is important to find all the available parallelism in programs. The Stanford SUIF interprocedural parallelizer we have developed is capable of detecting coarser granularity of parallelism in sequential scientific applications than previously possible. Specifically, it can parallelize loops that span numerous procedures and hundreds of lines of codes, frequently requiring modifications to array data structures such as array privatization. Measurements from several standard benchmark suites demonstrate that aggressive interprocedural analyses can substantially advance the capability of automatic parallelization technology. However, locating parallelism is not sufficient in achieving high performance. It is critical to make effective use of the memory hierarchy. In parallel applications, false sharing and cache conflicts between processors can significantly reduce performance. We have developed the first compiler that automatically performs a full suite of data transformations (a combination of transposing, strip-mining and padding). The performance of many benchmarks improves drastically after the data transformations. We introduce a framework based on systems of linear inequalities for developing compiler algorithms. Many of the whole program analyses and aggressive optimizations in our compiler employ this framework. Using this framework general solutions to many compiler problems can be found systematically.