Domain-Specific Acceleration and Auto-Parallelization of Legacy Scientific Code in FORTRAN 77 using Source-to-Source Compilation

TL;DR

This paper introduces an automated source-to-source compilation method that transforms legacy FORTRAN 77 scientific code into OpenCL-accelerated programs, enabling automatic parallelization and significant speedups on heterogeneous accelerators.

Contribution

It presents a novel approach for whole-program refactoring, data transfer minimization, and auto-parallelization of FORTRAN 77 code for heterogeneous accelerators.

Findings

Achieved up to 20x speedup on GPU for real-world codes.

Validated the approach on multiple scientific models and test suites.

Generated fully OpenCL-enabled code automatically.

Abstract

Massively parallel accelerators such as GPGPUs, manycores and FPGAs represent a powerful and affordable tool for scientists who look to speed up simulations of complex systems. However, porting code to such devices requires a detailed understanding of heterogeneous programming tools and effective strategies for parallelization. In this paper we present a source to source compilation approach with whole-program analysis to automatically transform single-threaded FORTRAN 77 legacy code into OpenCL-accelerated programs with parallelized kernels. The main contributions of our work are: (1) whole-source refactoring to allow any subroutine in the code to be offloaded to an accelerator. (2) Minimization of the data transfer between the host and the accelerator by eliminating redundant transfers. (3) Pragmatic auto-parallelization of the code to be offloaded to the accelerator by identification of parallelizable maps and reductions. We have validated the code transformation performance of the compiler on the NIST FORTRAN 78 test suite and several real-world codes: the Large Eddy Simulator for Urban Flows, a high-resolution turbulent flow model; the shallow water component of the ocean model Gmodel; the Linear Baroclinic Model, an atmospheric climate model and Flexpart-WRF, a particle dispersion simulator. The automatic parallelization component has been tested on as 2-D Shallow Water model (2DSW) and on the Large Eddy Simulator for Urban Flows (UFLES) and produces a complete OpenCL-enabled code base. The fully OpenCL-accelerated versions of the 2DSW and the UFLES are resp. 9x and 20x faster on GPU than the original code on CPU, in both cases this is the same performance as manually ported code.