Seminars
| Title | Conference | Dates | ||
|---|---|---|---|---|
| A journey towards end-to-end services for instrument based e-Science As data and computation assume increasing importance in modern science there is a need for integrated infrastructure that supports both basic and sophisticated workflows. To date there has been progress at building platforms that support part of this vision, however, there are often gaps that require manual intervention by trained users. In this seminar I describe the state of infrastructure at the University of Queensland, a work-in-progress journey aimed to realise the vision to support researchers with varying computing skills. We describe some of the components, namely a meta-data management and provisioning system called UQRDM; a sophisticated data storage platform called MeDiCI;, a novel instrument interface called CAMERA that facilitates processing on multiple compute platforms with minimal data duplication; domain specific data management repositories based on the OMERO, XNAT and CLOWDER tools; and multiple high throughput image processing portals called UQIPP and NIMROD. When used together these services allow a researcher to provision storage, capture data on a range of instruments, process it using multiple HPC and Cloud platforms; and publish the results for wider distribution. We will Illustrate the infrastructure by describing its use in multiple bioscience applications. | IWSG 2022 (14th International Workshop on Science Gateways) | June 2022 |  | |
| Translational Computer Science for e-Science Given the increasingly pervasive role and growing importance of computing and data in all aspects of science and society fundamental advances in computer science and their translation to the real world have become essential. Consequently, there may be benefits to formalizing Translational Computer Science (TCS) to complement the traditional foundational and applied modes of computer science research, as has been done for translational medicine. TCS has the potential to accelerate the impact of computer science research overall. In this talk I discuss the attributes of TCS, and formally define it. I enumerate a number of roadblocks that have limited its adoption to date and sketch a path forward. Finally, I will provide some specific examples of translational research underpinning eScience projects and illustrate the advantages to both computer science and the application domains. | IEEE eScience 2022 Keynote address | Oct 2022 | | |
| Translational Research Computer Science and its application to SupercomputingTranslational Computer Science (TCS) is an analogue of Translational Medicine and builds on three pillars: a laboratory where the work is performed; a locale where it is applied and a community who are engaged. The benefits include a shorter time to adoption, potentially improved research outcomes and deeper impact. In spite of a number of road blocks, researchers have used it informally to guide their work. For example, it was clear that Ken Kennedy used TCS – engaging in both deep theoretical research in language compilation for parallel supercomputers but translating it through practical compilers and tools. In this talk, I will provide personal experiences with TCS and how it has shaped my research in supercomputing. I will discuss two exemplar projects: one in distributed supercomputing and another in parallel debugging. I will highlight the role of community, especially the PRAGMA collaboration across the Pacific Rim. I will illuminate the role of undergraduate (and even high school) students in these activities, drawing on Ken Kennedy’s legacy for mentoring the next generation of researchers. | IEEE SC21 Ken Kennedy Award talk | Nov 2021 | | |
| Translational Research in Computer ScienceGiven the increasingly pervasive role and growing importance of computing and data in all aspects of science and society fundamental advances in computer science and their translation to the real world have become essential. Consequently, there may be benefits to formalizing translational computer science (TCS) to complement the traditional foundational and applied modes of computer science research, as has been done for translational medicine. TCS has the potential to accelerate the impact of computer science research overall. In this talk I discuss the attributes of TCS, and formally define it. I enumerate a number of roadblocks that have limited its adoption to date and sketch a path forward. Finally, I will provide some specific examples of translational research in parallel and distributed computing, drawing on personal research and also of others in the field. | RIVF (Research, Innovation and Vision for the Future) | October 2020 | | |
| Translational Research in Cluster ComputingGiven the increasingly pervasive role and growing importance of computing and data in all aspects of science and society fundamental advances in computer science and their translation to the real world have become essential. Consequently, there may be benefits to formalizing translational computer science (TCS) to complement the traditional foundational and applied modes of computer science research, as has been done for translational medicine. TCS has the potential to accelerate the impact of computer science research overall. In this talk I discuss the attributes of TCS, and formally define it. I enumerate a number of roadblocks that have limited its adoption to date and sketch a path forward. Finally, I will provide some specific examples of translational research in parallel and distributed computing, drawing on personal research and also of others in the field. | IEEE Cluster 2021 | September 2021 |  | |
| Scalable Distributed Infrastructure for Data Intensive ScienceModern research intensive organisations face challenges storing and preserving the increasing amounts of data generated by scientific instruments and high performance computers. Data must be delivered in a variety of modes depending on the end use, ranging from Web portals through to supercomputers. Building infrastructure to meet this need is complex and expensive. There is a need for mechanisms that support both managed and unmanaged data in a coherent and scalable way, often over a physically distributed multi-campus environment. In this talk I will discuss the ways we are delivering such infrastructure at the University of Queensland. Long term hierarchical storage, and many of the computing systems, are housed in a commercial Tier 3 data centre 20 kms from the main campus in St Lucia. Some high performance machines and desktops, and all scientific instruments, are housed on campus. University researchers work with local, national and international collaborators, requiring the need to share data securely and efficiently across a variety of scales. Our COTS based “MeDiCI data fabric” provides seamless access to data in such an environment. In order to improve standards of management, curation and preservation of data, a locally developed meta-data management service called RDM provides a single point of access for storage requests. Recent work on the CAMERA environment links unmanaged collections to managed repositories in a flexible and efficient manner. Finally, the fabric delivers data to a range of commodity and novel computing platforms such as the FlashLite data intensive cluster and the Wiener GPU supercomputer. | 5th International Conference on High Performance Compilation, Computing and Communications (HP3C’21) | June 2021 | ||
| Joint Conference on Computer Science and Software Engineering (JCSSE 2020) | May 2020 | |||
| Collaborative Conference on Computational and Data Intensive Science 2020 (C3DIS 2020) | March 2020 | | ||
| Just How Far Can We Push Symmetric Multiprocessing? Caches All The Way Down? | International Workshop on OpenMP, Auckland, New Zealand | September 2019 |