Fast GPU-based spot extraction for energy-dispersive X-ray Laue diffraction

作者：Alghabi F^*; Send S; Schipper U; Abboud A; Pashniak N; Pietsch U; Kolb A

来源：Journal of Instrumentation, 2014, 9(11): T11003.

DOI：10.1088/1748-0221/9/11/T11003

• Data processing methods
• Pattern recognition
• cluster finding
• calibration and fitting methods
• Analysis and statistical methods

This paper describes a novel method for fast online analysis of X-ray Laue spots taken by means of an energy-dispersive X-ray 2D detector. Current pnCCD detectors typically operate at some 100 Hz (up to a maximum of 400Hz) and have a resolution of 384 x 384 pixels, future devices head for even higher pixel counts and frame rates. %26lt;br%26gt;The proposed online data analysis is based on a computer utilizing multiple Graphics Processing Units (GPUs), which allow for fast and parallel data processing. Our multi-GPU based algorithm is compliant with the rules of stream-based data processing, for which GPUs are optimized. The paper%26apos;s main contribution is therefore an alternative algorithm for the determination of spot positions and energies over the full sequence of pnCCD data frames. Furthermore, an improved background suppression algorithm is presented. %26lt;br%26gt;The resulting system is able to process data at the maximum acquisition rate of 400 Hz. We present a detailed analysis of the spot positions and energies deduced from a prior (single-core) CPU-based and the novel GPU-based data processing, showing that the parallel computed results using the GPU implementation are at least of the same quality as prior CPU-based results. Furthermore, the GPU-based algorithm is able to speed up the data processing by a factor of 7 (in comparison to single-core CPU-based algorithm) which effectively makes the detector system more suitable for online data processing.

• 出版日期2014-11