A new parameter space study of cosmological microlensing

Cosmological gravitational microlensing is a useful technique for understanding the structure of the inner parts of a quasar, especially the accretion disk and the central supermassive black hole. So far, most of the cosmological microlensing studies have focused on single objects from ~90 currently known lensed quasars. However, present and planned all-sky surveys are expected to discover thousands of new lensed systems. Using a graphics processing unit (GPU) accelerated ray-shooting code, we have generated 2550 magnification maps uniformly across the convergence ({\kappa}) and shear ({\gamma}) parameter space of interest to microlensing. We examine the effect of random realizations of the microlens positions on map properties such as the magnification probability distribution (MPD). It is shown that for most of the parameter space a single map is representative of an average behaviour. All of the simulations have been carried out on the GPU-Supercomputer for Theoretical Astrophysics Research (gSTAR).Comment: 16 pages, 10 figures, accepted for publication in MNRA

Data Compression in the Petascale Astronomy Era: a GERLUMPH case study

As the volume of data grows, astronomers are increasingly faced with choices on what data to keep -- and what to throw away. Recent work evaluating the JPEG2000 (ISO/IEC 15444) standards as a future data format standard in astronomy has shown promising results on observational data. However, there is still a need to evaluate its potential on other type of astronomical data, such as from numerical simulations. GERLUMPH (the GPU-Enabled High Resolution cosmological MicroLensing parameter survey) represents an example of a data intensive project in theoretical astrophysics. In the next phase of processing, the ~27 terabyte GERLUMPH dataset is set to grow by a factor of 100 -- well beyond the current storage capabilities of the supercomputing facility on which it resides. In order to minimise bandwidth usage, file transfer time, and storage space, this work evaluates several data compression techniques. Specifically, we investigate off-the-shelf and custom lossless compression algorithms as well as the lossy JPEG2000 compression format. Results of lossless compression algorithms on GERLUMPH data products show small compression ratios (1.35:1 to 4.69:1 of input file size) varying with the nature of the input data. Our results suggest that JPEG2000 could be suitable for other numerical datasets stored as gridded data or volumetric data. When approaching lossy data compression, one should keep in mind the intended purposes of the data to be compressed, and evaluate the effect of the loss on future analysis. In our case study, lossy compression and a high compression ratio do not significantly compromise the intended use of the data for constraining quasar source profiles from cosmological microlensing.Comment: 15 pages, 9 figures, 5 tables. Published in the Special Issue of Astronomy & Computing on The future of astronomical data format

Accelerating the Rate of Astronomical Discovery with GPU-Powered Clusters

In recent years, the Graphics Processing Unit (GPU) has emerged as a low-cost alternative for high performance computing, enabling impressive speed-ups for a range of scientific computing applications. Early adopters in astronomy are already benefiting in adapting their codes to take advantage of the GPU's massively parallel processing paradigm. I give an introduction to, and overview of, the use of GPUs in astronomy to date, highlighting the adoption and application trends from the first ~100 GPU-related publications in astronomy. I discuss the opportunities and challenges of utilising GPU computing clusters, such as the new Australian GPU supercomputer, gSTAR, for accelerating the rate of astronomical discovery.Comment: To appear in the proceedings of ADASS XXI, ed. P.Ballester and D.Egret, ASP Conf. Se

Advanced Architectures for Astrophysical Supercomputing

Astronomers have come to rely on the increasing performance of computers to reduce, analyze, simulate and visualize their data. In this environment, faster computation can mean more science outcomes or the opening up of new parameter spaces for investigation. If we are to avoid major issues when implementing codes on advanced architectures, it is important that we have a solid understanding of our algorithms. A recent addition to the high-performance computing scene that highlights this point is the graphics processing unit (GPU). The hardware originally designed for speeding-up graphics rendering in video games is now achieving speed-ups of $O(100\times)$ in general-purpose computation -- performance that cannot be ignored. We are using a generalized approach, based on the analysis of astronomy algorithms, to identify the optimal problem-types and techniques for taking advantage of both current GPU hardware and future developments in computing architectures.Comment: 4 pages, 1 figure, to appear in the proceedings of ADASS XIX, Oct 4-8 2009, Sapporo, Japan (ASP Conf. Series

GPU-Based Volume Rendering of Noisy Multi-Spectral Astronomical Data

Traditional analysis techniques may not be sufficient for astronomers to make the best use of the data sets that current and future instruments, such as the Square Kilometre Array and its Pathfinders, will produce. By utilizing the incredible pattern-recognition ability of the human mind, scientific visualization provides an excellent opportunity for astronomers to gain valuable new insight and understanding of their data, particularly when used interactively in 3D. The goal of our work is to establish the feasibility of a real-time 3D monitoring system for data going into the Australian SKA Pathfinder archive. Based on CUDA, an increasingly popular development tool, our work utilizes the massively parallel architecture of modern graphics processing units (GPUs) to provide astronomers with an interactive 3D volume rendering for multi-spectral data sets. Unlike other approaches, we are targeting real time interactive visualization of datasets larger than GPU memory while giving special attention to data with low signal to noise ratio - two critical aspects for astronomy that are missing from most existing scientific visualization software packages. Our framework enables the astronomer to interact with the geometrical representation of the data, and to control the volume rendering process to generate a better representation of their datasets.Comment: 4 pages, 1 figure, to appear in the proceedings of ADASS XIX, Oct 4-8 2009, Sapporo, Japan (ASP Conf. Series

Spotting Radio Transients with the help of GPUs

Exploration of the time-domain radio sky has huge potential for advancing our knowledge of the dynamic universe. Past surveys have discovered large numbers of pulsars, rotating radio transients and other transient radio phenomena; however, they have typically relied upon off-line processing to cope with the high data and processing rate. This paradigm rules out the possibility of obtaining high-resolution base-band dumps of significant events or of performing immediate follow-up observations, limiting analysis power to what can be gleaned from detection data alone. To overcome this limitation, real-time processing and detection of transient radio events is required. By exploiting the significant computing power of modern graphics processing units (GPUs), we are developing a transient-detection pipeline that runs in real-time on data from the Parkes radio telescope. In this paper we discuss the algorithms used in our pipeline, the details of their implementation on the GPU and the challenges posed by the presence of radio frequency interference.Comment: 4 Pages. To appear in the proceedings of ADASS XXI, ed. P.Ballester and D.Egret, ASP Conf. Serie