Student research opportunities
Advanced Parallelization Techniques and Supporting Infrastructure for large-scale Climate Science Simulations
Project Code: CECS_629
This project is available at the following levels:
Honours, Masters, PhD
Keywords:
Parallel computing, climate change science, climate simulation
Supervisor:
Assoc Professor Peter StrazdinsOutline:
Climate and weather models currently consume vast amounts of supercomputer time, with the most dominant component being the atmosphere. In order to make accurate long-range forecasts, BoM requires high resolution global atmosphere models.Similarly with the ACCESS project performing large-scale climate simulations, the amount of usage is exploding.
However, these models are complex software systems, with large amounts of legacy code. The primary consideration is to correctly encode the science for meaningful simulations; the secondary is performance, particularly on large-scale parallel computers. As a result, generally only the simplest parallelization strategies are reflected in current software. This project will explore new techniques for the optimization and parallelization of current climate model codes, including the exploration of new programming paradigms for heterogeneous manycore systems.
A second (possibly alternate) theme to the project would be to develop supporting infrastructure in order to optimize such codes reliably. This is non-trivial due to the complexity and the long history of such large-scale numerical simulations.
Goals of this project
The goals of this project include (1) analyzing and developing an understanding of the performance and scaling behaviour of a selection of climate model codes, (2) exploring and evaluating new opportunities and techniques for parallelization. This includes the use of new programming paradigms and the use of accelerators such as GPUs and the Xeon Phi.
Alternate algorithms to solve the underlying PDEs that offer better scalability properties such as using explicit methods could also be investigated.
Work on supporting infrastructure includes automated methods to reverse-engineer test harnesses (correctness and performance) for selected performance-critical subroutines.
Requirements/Prerequisites
(PhD Level) An Honours degree in computer or computational science or equivalent. Some background in high performance computing and mathematical modelling is desirable.
Student Gain
Climate science is of increasing importance, and the with it the need to perform efficient and meaningful simulations, especially for medium to long term forecasts. This project represents an opportunity to join and make a significant contribution a international team working in climate science, including the Bureau of Meteorology, CSIRO, NVIDIA and Shanghai Jiatong University.
PhD Scholarships, or top-ups on existing scholarships will be available from 2016.
