User-friendly programming of GPU-enhanced clusters via automated code translation and optimization

This project aims to help computational scientists to embrace the exceptional computing power of modern computer clusters that are accelerated with GPUs and/or many-integrated-core coprocessors. In particular, we are investigating new programming methodologies that match with these latest hardware developments. Effort has also been directed to creating an automated source-to-source compiler, which takes as input serial C code and generates as output hybrid MPI-OpenMP-CUDA code that can utilise modern heterogeneous clusters. Moreover, we have applied the developed methodologies and tools to real-world scientific applications. The most successful application is about simulating sub-cellular calcium dynamics on the world's most powerful supercomputer: Tianhe-2.

By developing a simple directive-based programming model and its accompanying fully automated source-to-source code translator and domain-specific optimizer, we aim to greatly simplify the task of programming scientific codes that can run efficiently on a ccelerator-enhanced computer clusters. This project is motivated by an urgent need from the community of computational scientists for programming methodologies that are easy to use, while capable of harnessing especially the non-conventional computing res ources, such as GPUs, that dominate today's HPC field. Based on a proof-of-concept work that has already successfully automated C-to-CUDA translation and optimization restricted to the single-GPU scenario and stencil methods, the proposed project aims to greatly enhance the success by extending to the following topics: (1) improving the newly developed directive-based programming model and its accompanying framework of automated code translation and optimization, (2) including finite element methods and p article methods as two new application domains, (3) extending to the scenario of multiple GPUs, (4) extending to the scenario of GPU-accelerated CPU clusters, (5) tackling a number of real-world scientific codes. The project has the potential of considera bly enhancing the productivity of computational scientists, to let them focus more on their scientific investigations at hand, instead of spending precious time on painstakingly writing complex codes.