New High Performance GPGPU Code Transformation Framework Applied to Large Production Weather Prediction Code

TL;DR

Hybrid Fortran is a novel framework enabling high-performance GPGPU porting of structured grid Fortran weather models with minimal code changes, achieving performance comparable or superior to existing methods like OpenACC.

Contribution

The paper presents Hybrid Fortran, a new approach that simplifies GPGPU porting for large Fortran weather models with minimal modifications, improving productivity and performance.

Findings

Hybrid Fortran matches or exceeds OpenACC performance.

It enables GPGPU porting with minimal code changes.

Replaces over 50 CPU cores with 24 GPUs in weather simulations.

Abstract

We introduce "Hybrid Fortran", a new approach that allows a high performance GPGPU port for structured grid Fortran codes. This technique only requires minimal changes for a CPU targeted codebase, which is a significant advancement in terms of productivity. It has been successfully applied to both dynamical core and physical processes of ASUCA, a Japanese mesoscale weather prediction model with more than 150k lines of code. By means of a minimal weather application that resembles ASUCA's code structure, Hybrid Fortran is compared to both a performance model as well as today's commonly used method, OpenACC. As a result, the Hybrid Fortran implementation is shown to deliver the same or better performance than OpenACC and its performance agrees with the model both on CPU and GPU. In a full scale production run, using an ASUCA grid with 1581 x 1301 x 58 cells and real world weather data in 2km resolution, 24 NVIDIA Tesla P100 running the Hybrid Fortran based GPU port are shown to replace more than 50 18-core Intel Xeon Broadwell E5-2695 v4 running the reference implementation - an achievement comparable to more invasive GPGPU rewrites of other weather models.