Abstract
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.
This research is partially supported by the AstroSTREAM project, funded by NWO/STARE, and the SCALP project, funded by STW/Progress.
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Keywords
- High Performance Computing
- Cell Processor
- Support Kernel
- High Performance Computing Application
- Cell Broadband Engine
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Varbanescu, A.L. et al. (2008). Radioastronomy Image Synthesis on the Cell/B.E.. In: Luque, E., Margalef, T., Benítez, D. (eds) Euro-Par 2008 – Parallel Processing. Euro-Par 2008. Lecture Notes in Computer Science, vol 5168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85451-7_80
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DOI: https://doi.org/10.1007/978-3-540-85451-7_80
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