GLAF: A Visual Programming and Auto-tuning Framework for Parallel Computing

Abstract

The past decade's computing revolution has delivered parallel hardware to the masses. However, the ability to exploit its capabilities and ignite scientific breakthrough at a proportionate level remains a challenge due to the lack of parallel programming expertise. Although different solutions have been proposed to facilitate harvesting the seeds of parallel computing, most target seasoned programmers and ignore the special nature of a target audience like domain experts. This paper addresses the challenge of realizing a programming abstraction and implementing an integrated development framework for this audience. We present GLAF -- a grid-based language and auto-parallelizing, auto-tuning framework. Its key elements are its intuitive visual programming interface, which attempts to render expressing and validating an algorithm easier for domain experts, and its ability to automatically generate efficient serial and parallel Fortran and C code, including potentially beneficial code modifications (e.g., With respect to data layout). We find that the above features assist novice programmers to avoid common programming pitfalls and provide fast implementations.