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A.S. van Amesfoort
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The particle filter is a Bayesian estimation technique based on Monte Carlo simulation. The nonparametric nature of particle filters makes them ideal for non-linear, non-Gaussian dynamic systems. Particle filtering has many applications: in computer vision, robotics, and econometrics to name just a few. Although superior to Kalman filters, particle filters have higher computational requirements, which limits practical use in real-time applications. In this paper, we investigate how to design a particle filter framework for complex real-time estimation problems using modern many-core architectures. We develop a robotic arm application that serves as a highly flexible estimation problem to push estimation rates and accuracy to new levels. By varying different filter and model parameters, we derive rules of thumb for good filter configurations. We evaluate our particle filter with a comprehensive performance and correctness analysis. Our results significantly lower the development effort of particle filters for other real-time estimation problems. For the most demanding robotic arm configuration, we can process one million particles at an update rate of a few hundred state estimations per second. As such, we see our results as a step towards wider adoption of particle filters, and as a prerequisite to investigate larger filter setups for even more complex estimation problems.
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The particle filter is a Bayesian estimation technique based on Monte Carlo simulation. The nonparametric nature of particle filters makes them ideal for non-linear, non-Gaussian dynamic systems. Particle filtering has many applications: in computer vision, robotics, and econometrics to name just a few. Although superior to Kalman filters, particle filters have higher computational requirements, which limits practical use in real-time applications. In this paper, we investigate how to design a particle filter framework for complex real-time estimation problems using modern many-core architectures. We develop a robotic arm application that serves as a highly flexible estimation problem to push estimation rates and accuracy to new levels. By varying different filter and model parameters, we derive rules of thumb for good filter configurations. We evaluate our particle filter with a comprehensive performance and correctness analysis. Our results significantly lower the development effort of particle filters for other real-time estimation problems. For the most demanding robotic arm configuration, we can process one million particles at an update rate of a few hundred state estimations per second. As such, we see our results as a step towards wider adoption of particle filters, and as a prerequisite to investigate larger filter setups for even more complex estimation problems.
Report
(2008)
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A.L. Varbanescu, A.S. van Amesfoort, H.J. Sips, T Cornwell, B. Elmegreen, A Mattingly, G van Diepen, Rob van Nieuwpoort
In this report we present our experience with porting two very time consuming kernels from radioastronomy imaging, i.e. the gridding/degridding operations. Both these procedures are implemented using convolutional resampling, a bandwidth-limited application which is not trivial to parallelize on a hybrid memory architecture. We briefly discuss the role of the application in the radioastronomy context, and we present the Cell/B.E. multi-core processor as an interesting target for this application. Further, we show how the original reference implementation (sequential, C++ code) was not “Cell-friendly”, which is also why our first parallel version had poor results. Next, we show how the reference algorithm was tuned to become more suitable for Cell parallelization. We discuss how we have improved our parallelization strategy and we present our performance results with the new tuned algorithm running on the Cell/B.E. processor, showing a speed-up factor of over 20 when compared with the reference implementation. We conclude that a single Cell/B.E. can provide good results for a small-scale version of the radioastronomy imaging problem, and we sketch our future work directions towards using a parallel machine with multiple Cell/B.E. processors to address the real scale of the of the gridding/degridding applications.
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In this report we present our experience with porting two very time consuming kernels from radioastronomy imaging, i.e. the gridding/degridding operations. Both these procedures are implemented using convolutional resampling, a bandwidth-limited application which is not trivial to parallelize on a hybrid memory architecture. We briefly discuss the role of the application in the radioastronomy context, and we present the Cell/B.E. multi-core processor as an interesting target for this application. Further, we show how the original reference implementation (sequential, C++ code) was not “Cell-friendly”, which is also why our first parallel version had poor results. Next, we show how the reference algorithm was tuned to become more suitable for Cell parallelization. We discuss how we have improved our parallelization strategy and we present our performance results with the new tuned algorithm running on the Cell/B.E. processor, showing a speed-up factor of over 20 when compared with the reference implementation. We conclude that a single Cell/B.E. can provide good results for a small-scale version of the radioastronomy imaging problem, and we sketch our future work directions towards using a parallel machine with multiple Cell/B.E. processors to address the real scale of the of the gridding/degridding applications.
Conference paper
(2008)
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A.L. Varbanescu, A.S. van Amesfoort, T Cornwell, A Mattingly, B. Elmegreen, Rob van Nieuwpoort, G van Diepen, H.J. Sips
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.
...
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.