Now that large radiotelescopes like SKA, LOFAR, or ASKAP, become available in different parts of the world, radioastronomers foresee a vast increase in the amount of data to gather, store and process. To keep the processing time bounded, parallelization and execution on (massively) parallel machines are required for the commonly-used radioastronomy software kernels. In this paper, we analyze data gridding and degridding, a very time-consuming kernel of radioastronomy image synthesis. To tackle its its dynamic behavior, we devise and implement a parallelization strategy for the Cell/B.E. multi-core processor, offering a cost-efficient alternative compared to classical supercomputers. Our experiments show that the application running on one Cell/B.E. is more than 20 times faster than the original application running on a commodity machine. Based on scalability experiments, we estimate the hardware requirements for a realistic radio-telescope. We conclude that our parallelization solution exposes an efficient way to deal with dynamic data-intensive applications on heterogeneous multi-core processors. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Varbanescu, A. L., Van Amesfoort, A. S., Cornwell, T., Mattingly, A., Elmegreen, B. G., Van Nieuwpoort, R., … Sips, H. (2008). Radioastronomy image synthesis on the cell/B.E. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5168 LNCS, pp. 749–762). https://doi.org/10.1007/978-3-540-85451-7_80
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