Extending a family of garbage collectors. Garbage collection is a key component in the automatic management of storage in computer systems. It is an essential property of modern programming systems that frees the programmer from a significant error-prone task. Our interest is in garbage collection in distributed systems involving a number of networked computers. Using our novel construction methodology, we have jointly produced a family of collection algorithms that are significantly simpler and ....Extending a family of garbage collectors. Garbage collection is a key component in the automatic management of storage in computer systems. It is an essential property of modern programming systems that frees the programmer from a significant error-prone task. Our interest is in garbage collection in distributed systems involving a number of networked computers. Using our novel construction methodology, we have jointly produced a family of collection algorithms that are significantly simpler and more efficient than previous work. Here we wish to extend this family to operate effectively in a specific architecture increasingly favoured by many modern distributed high-performance computing systems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100967
Funder
Australian Research Council
Funding Amount
$366,000.00
Summary
Open-world computer vision by detecting and tracking hierarchical objects. This project examines the problem of detecting and tracking objects using computer vision. A fundamental limitation of current algorithms is that they require labelled training data for every object class and therefore cannot be trusted to operate in unconstrained environments. This project aims to address this limitation using novel techniques that incorporate hierarchical relationships between object classes. Expected o ....Open-world computer vision by detecting and tracking hierarchical objects. This project examines the problem of detecting and tracking objects using computer vision. A fundamental limitation of current algorithms is that they require labelled training data for every object class and therefore cannot be trusted to operate in unconstrained environments. This project aims to address this limitation using novel techniques that incorporate hierarchical relationships between object classes. Expected outcomes include new paradigms for algorithm design and evaluation, and establishing the problem as a focus of international research. The key practical benefit would be to accelerate the wider deployment of visual perception in applications such as autonomous vehicles, interactive robotics, and video analysis.Read moreRead less
Accurate Performance Modelling and Prediction of Cluster Computers. The tools, methodologies and data produced by this project will assist
Australian academic and industrial organisations in choosing the most
cost-effective cluster configurations for their specific high
performance computing requirements. It will also help an Australian
company to compete with increasing strength against the major
multinationals. The project will also draw together and promote future
research links between ....Accurate Performance Modelling and Prediction of Cluster Computers. The tools, methodologies and data produced by this project will assist
Australian academic and industrial organisations in choosing the most
cost-effective cluster configurations for their specific high
performance computing requirements. It will also help an Australian
company to compete with increasing strength against the major
multinationals. The project will also draw together and promote future
research links between two major academic institutions in this field.
Finally, the project will provide high-level training in research,
with industrial grounding, in the high performance computing industry.
Read moreRead less