A Massive Data Parallel Computational Framework for Petascale/Exascale Hybrid Computer Systems

TL;DR

This paper introduces a new massively data parallel computational framework designed for petascale and exascale hybrid HPC systems, demonstrated through a high-performance 3D CFD code.

Contribution

It presents an alternative framework for developing scalable scientific applications on hybrid systems, improving performance over existing methods.

Findings

Successfully developed a 3D CFD code with improved performance

Demonstrated the framework's scalability on hybrid HPC systems

Provides a new approach for data parallel scientific computing

Abstract

Heterogeneous systems are becoming more common on High Performance Computing (HPC) systems. Even using tools like CUDA and OpenCL it is a non-trivial task to obtain optimal performance on the GPU. Approaches to simplifying this task include Merge (a library based framework for heterogeneous multi-core systems), Zippy (a framework for parallel execution of codes on multiple GPUs), BSGP (a new programming language for general purpose computation on the GPU) and CUDA-lite (an enhancement to CUDA that transforms code based on annotations). In addition, efforts are underway to improve compiler tools for automatic parallelization and optimization of affine loop nests for GPUs and for automatic translation of OpenMP parallelized codes to CUDA. In this paper we present an alternative approach: a new computational framework for the development of massively data parallel scientific codes applications suitable for use on such petascale/exascale hybrid systems built upon the highly scalable Cactus framework. As the first non-trivial demonstration of its usefulness, we successfully developed a new 3D CFD code that achieves improved performance.