Radioastronomy Image Synthesis on the Cell/B.E.
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Evaluating multi-core platforms for HPC data-intensive kernels
Proceedings of the 6th ACM conference on Computing frontiers
Using many-core hardware to correlate radio astronomy signals
Proceedings of the 23rd international conference on Supercomputing
DOME: towards the ASTRON & IBM center for exascale technology
Proceedings of the 2012 workshop on High-Performance Computing for Astronomy Date
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This paper presents a novel work-distribution strategy for GPUs, that efficiently convolves radio-telescope data onto a grid, one of the most time-consuming processing steps to create a sky image. Unlike existing work-distribution strategies, this strategy keeps the number of device-memory accesses low, without incurring the overhead from sorting or searching within telescope data. Performance measurements show that the strategy is an order of magnitude faster than existing accelerator-based gridders. We compare CUDA and OpenCL performance for multiple platforms. Also, we report very good multi-GPU scaling properties on a system with eight GPUs, and show that our prototype implementation is highly energy efficient. Finally, we describe how a unique property of GPUs, fast texture interpolation, can be used as a potential way to improve image quality.