Abstract
AbstractCurve fitting is a fundamental task in many research fields. In this paper we present results demonstrating the fitting of 2D images using CUDA (compute unified device architecture) on NVIDIA graphics processors via particle swarm optimization (PSO). Particle swarm optimization is particularly well-suited to implementation on graphics processors using CUDA as each CUDA thread can be made to model a single particle in a swarm with the swarm itself defined by thread blocks.The motivation for this work was the reconstruction of interferometric photoactivated localization microscopy (iPALM) data sets. The reconstruction requires the fitting of 2D curves to potentially millions of detected photoactivation peaks. Additional motivation was to search for a solution that replaces a cluster with a single desktop machine using multiple CUDA graphics cards.PSO curve fitting running on the GPU enabled a substantial performance increase over the CPU alone and scaled well with multiple CUDA cards. The performanc...
Highlights
Interferometric photoactivated localization microscopy is a novel light microscopy technique for imaging cellular ultrastructure. iPALM can generate 3D images of the distribution of properly tagged molecules [1]
To make iPALM microscopes more accessible to other researchers, to replace the cluster with NVIDIA graphics cards using CUDA; Co-published by Atlantis Press and Taylor & Francis Copyright: the authors 213 this work describes a step towards that goal
We describe the actual implementation of the PSFIT algorithm. After this we present results demonstrating the performance gains achieved with the CUDA implementation
Summary
Interferometric photoactivated localization microscopy (iPALM) is a novel light microscopy technique for imaging cellular ultrastructure. iPALM can generate 3D images of the distribution of properly tagged molecules [1]. A key step in the iPALM reconstruction process requires fitting images representing detected photoactivation peaks to a 2D Gaussian surface. This fitting must be done for each of the hundreds of thousands to several millions of peaks in the iPALM data. The cluster runs IDL sessions to do the curve fitting. It is desired, to make iPALM microscopes more accessible to other researchers, to replace the cluster with NVIDIA graphics cards using CUDA; Co-published by Atlantis Press and Taylor & Francis Copyright: the authors 213 this work describes a step towards that goal
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