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 and ocean 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. State-of-the-art supercomputers are becoming increasingly complex, with nodes not only being made of highly multiple traditional processing cores, but with multiple manycore accelerators.
The aim of the research would be to investigate techniques to support the efficient porting of key codes used in weather forecasting to supercomputers with heterogeneous nodes, that is multicore main processors augmented with accelerators such as NVIDIA's General Purpose Graphics Processing Units (GP-GPUs).
There are two levels to this project (A) investigating individual applications, and manually improving performance, and (B) a systematic evaluation of a gropup of applications, wiht the aim of (developing tools that support semi-)automatic code parallelization.
Level A is suitable for advanced year coursework to Honours projects; level B is suited to Honours to HDR projects.
A specific application at Level A is the HYSPLIT, a particle-tracking application. This is one of the most widely-used codes in the world,
and would have been in heavy use predicting smoke dispersion in the recent eastern Australian bushfires. A superb parallrlization and CUDA port was performed by Honours student Fan yu in 2018. BoM have been evaluating these codes for over two years now, andhave prepared them for deployment in the 2021 stage in the international Volcanic Ash Modelling Project. However, further development of these codes is desired, for example a multi-GPU version.
The goals of this project include (1) analyzing and developing an undersanding of the performance and scaling behavior of a selection of operational weather codes, (2) exploring and evaluating new opportunities and techniques for parallelization, in particular wiht the use of GPU accelerators. . At level B, this includes the use of new programming paradigms and code transformation tools. Work on supporting infrastructure includes automated methods to reverse-engineer test harnesses (correctness and performance) for selected performance-critical subroutines and generate kernels for them, methods to automatically refactor the codes for the desired target node architecture, and tools to reliably predict the performance of these codes on future accelerator designs. This infrastructure and techniques can be used to benefit many applications areas, but the project will aim to prove them on selected operational codes of interest to the Bureau of Meteorology and NVIDIA.
(PhD Level) An Honours degree in computer or computational science or equivalent. Some background in high performance computing is highly desirable. Experience in code transformations tools would be ideal.
(Coursework project and Honours level) at least two years study in Computer Science, Some background in high performance computing is highly desirable
This is a computer science project, knowledge in weather and earth sciences is not required.
Fan Yu, Peter E. Strazdins, Joerg Henrichs and Tim F. Pugh. Shared Memory and GPU Parallelization of an Operational Atmospheric Transportand Dispersion Application, 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) pp 729-738, IEEE, May 2019.
Peter E. Strazdins, Margaret Kahn, Joerg Henrichs, Tim Pugh and Mike Rezny, Profiling Methodology and Performance Tuning of the Met Office Unified Model for Weather and Climate Simulations , Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium Workshops, Anchorage, May 2011, pp1317-1326.
Weather science is of increasing importance, and the with it the need to perform efficient and meaningful simulations, especially for medium-term forecasts. This project represents an opportunity to join and make a significant contribution with an international team working in computer and earth sciences.
Over 2017-18, Honours student Fan Yu achieved the first OpenMP and CUDA parallelization of the widely used HYPSLIT particle tracking model (https://www.ready.noaa.gov/HYSPLIT.php), widely used around the world for tracking pollutant dispersion (e.g. from bushfires or the Fukujima 2011 disaster). Fan's accelerated codes are now being used operationally in two international projects ( https://cecs.anu.edu.au/news/new-weather-code-sunn...), but there is scope for up to an honours project on further improvements.