Application of Automatic Parallelization to Modern Challenges of Scientific Computing Industries

Characteristics of full applications found in scientific computing industries today lead to challenges that are not addressed by state-of-the-art approaches to automatic parallelization.These characteristics are not present in CPU kernel codes nor linear algebra libraries, requiring a fresh look at how to make automatic parallelization apply to today's computational industries using full applications. The challenges to automatic parallelization result from software engineering patterns that implement multifunctionality, reusable execution frameworks, data structures shared across abstract programming interfaces, a multilingual code base for a single application, and the observation that full applications demand more from compile-time analysis than CPU kernel codes do. Each of these challenges has a detrimental impact on compile-time analysis required for automatic parallelization. Then, focusing on a set of target loops that are parallelizable by hand and that result in speedups on par with the distributed parallel version of the full applications, we determine the prevalence of a number of issues that hinder automatic parallelization. These issues point to enabling techniques that are missing from the state-of-the-art.In order for automatic parallelization to become utilizedin today's scientific computing industries, the challenges described in this paper must be addressed.