Radioastronomy Image Synthesis on the Cell/B.E.

Authors:
Ana Lucia Varbanescu;Alexander S. Amesfoort;Tim Cornwell;Andrew Mattingly;Bruce G. Elmegreen;Rob Nieuwpoort;Ger Diepen;Henk Sips
Affiliations:
Delft University of Technology, The Netherlands and IBM Research, T.J.Watson Research Center, NY, USA;Delft University of Technology, The Netherlands;Australia Telescope National Facility,;IBM, ST Leonards, NSW, Australia;IBM Research, T.J.Watson Research Center, NY, USA;ASTRON, Dwingeloo, The Netherlands;ASTRON, Dwingeloo, The Netherlands;Delft University of Technology, The Netherlands
Venue:
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Year:
2008

Citing 6
Cited 2

Power Efficient Processor Architecture and The Cell Processor

HPCA '05 Proceedings of the 11th International Symposium on High-Performance Computer Architecture
The potential of the cell processor for scientific computing

Proceedings of the 3rd conference on Computing frontiers
MPI Microtask for programming the cell broadband engineTM processor

IBM Systems Journal
Sequoia: programming the memory hierarchy

Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Sequoia: programming the memory hierarchy

Proceedings of the 2006 ACM/IEEE conference on Supercomputing
The cell broadband engine: exploiting multiple levels of parallelism in a chip multiprocessor

International Journal of Parallel Programming

A Parallel Point Matching Algorithm for Landmark Based Image Registration Using Multicore Platform

Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
An efficient work-distribution strategy for gridding radio-telescope data on GPUs

Proceedings of the 26th ACM international conference on Supercomputing

Quantified Score

Hi-index	0.00

Visualization

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.