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- W2962798218 abstract "Modern astronomy has come to depend on the exponential progress of computing technology. In recent times, however, processor hardware has undergone a dramatic shift away from the traditional, sequential model of computing toward a new, massively parallel model. Due to the preponderance of unparallelised software in astronomy, this development poses significant challenges for the community. This thesis explores the substantial benefits offered by advanced ‘many-core’ computing architectures and advocates a powerful, general approach to their use; these concepts are then put into practice to achieve new science outcomes in the field of pulsar astronomy. We begin by developing a methodology for tackling the challenges of massively-parallel computing based on the analysis of algorithms. Simple analysis techniques are shown to provide deep insight into both the suitability of particular problems for advanced architectures as well as the optimal implementation approach when targeting such hardware. The method is applied to four well-known astronomy applications, highlighting their scalability and resulting in the rapid identification of potential speed-ups from cheaply-available many-core devices. The hardwareand software-independent nature of our approach means that, like a mathematical proof, such results remain valid in perpetuity. Building on this foundation, we then consider in more detail the process of incoherent dedispersion, a computationally-intensive problem at the heart of surveys for fast radio transients. Three different dedispersion algorithms are analysed and implemented for a particular form of many-core hardware, the graphics processing unit (GPU), and speedups of up to nine times are obtained when compared to an efficient multi-core CPU implementation. The GPU-based direct dedispersion code is shown to enable processing of data from the High Time Resolution Universe (HTRU) survey, currently ongoing at the CSIRO Parkes 64 m radio telescope in New South Wales, Australia, at a rate three times faster than real time. We look toward GPU-driven scientific outcomes by developing a real-time fast-radiotransient detection pipeline capable of exploiting many-core computing architectures. Our GPU dedispersion code is combined with new data-parallel and statistically robust implementations of algorithms for radio-frequency-interference (RFI) mitigation, baseline removal, normalisation, matched filtering and event detection to form a complete system capable of sustained real-time operation. The pipeline is demonstrated using both archival data from the HTRU survey and real-time observations at Parkes Observatory, where it has been deployed as part of the Berkeley Parkes Swinburne Recorder back-end. Early" @default.
- W2962798218 created "2019-07-30" @default.
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- W2962798218 date "2010-01-01" @default.
- W2962798218 modified "2023-10-16" @default.
- W2962798218 title "Advanced architectures for astrophysical supercomputing" @default.
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