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.