Accelerating Laue Depth Reconstruction Algorithm with CUDA

TL;DR

This paper presents a GPU-accelerated solution for Laue depth reconstruction, significantly reducing processing time from weeks to hours, enabling faster analysis of material structures.

Contribution

The paper introduces a scalable CUDA-based GPU implementation that accelerates Laue depth reconstruction, outperforming traditional CPU methods.

Findings

Reconstruction time reduced by 10-20 times

GPU implementation is scalable for various data sizes

Significant speedup enables more efficient data analysis

Abstract

The Laue diffraction microscopy experiment uses the polychromatic Laue micro-diffraction technique to examine the structure of materials with sub-micron spatial resolution in all three dimensions. During this experiment, local crystallographic orientations, orientation gradients and strains are measured as properties which will be recorded in HDF5 image format. The recorded images will be processed with a depth reconstruction algorithm for future data analysis. But the current depth reconstruction algorithm consumes considerable processing time and might take up to 2 weeks for reconstructing data collected from one single experiment. To improve the depth reconstruction computation speed, we propose a scalable GPU program solution on the depth reconstruction problem in this paper. The test result shows that the running time would be 10 to 20 times faster than the prior CPU design for various size of input data.