Bicer, T. | Search NASA

DOE OSTI2015

Maximum a posteriori estimation of crystallographic phases in X-ray diffraction tomography

A maximum a posteriori approach is proposed for X-ray diffraction tomography for reconstructing three-dimensional spatial distribution of crystallographic phases and orientations of polycrystalline materials. The approach maximizes the a posteriori density which includes a Poisson log-likelihood and an a priori term that reinforces expected solution properties such as smoothness or local continuity. The reconstruction method is validated with experimental data acquired from a section of the spinous process of a porcine vertebra collected at the 1-ID-C beamline of the Advanced Photon Source, at Argonne National Laboratory. The reconstruction results show significant improvement in the reduction of aliasing and streaking artefacts, and improved robustness to noise and undersampling compared to conventional analytical inversion approaches. The approach has the potential to reduce data acquisition times, and significantly improve beamtime efficiency.

Biomineralization↗

DOE OSTI2013

Integrating Online Compression to Accelerate Large-Scale Data Analytics Applications

Abstract—With growing dataset sizes, and as computing cycles are increasing faster than storage and wide-area bandwidths, compression appears like a promising approach for improving the performance of large-scale data analytics applications. In this context, this paper makes the following contributions. First, we develop a new compression methodology, which exploits the similarities between spatial and/or temporal neighbors in a simulation dataset, and enables high compression ratios and low decompression costs. Second, we have developed a framework that can be used to incorporate a variety of compression and decompression algorithms. This framework also supports a simple API to allow integration with an existing application or data processing middleware. Once a compression algorithm is implemented, this framework can allow multi-threaded retrieval, multi-threaded data decompression, and use of informed prefetching and caching. By integrating this framework with a data-intensive middleware, we have applied our compression methodology and framework to three applications over two datasets, including a GCRM climate model dataset. We obtained an average compression ratio of 51.68%, and up to 53.27% improvement in execution time of data analysis applications.

Big Data, I/O, HPC↗