Talita Perciano

SHARP: a distributed, GPU-based ptychographic solver

Ever brighter light sources, fast parallel detectors, and advances in phase retrieval methods, have made ptychography a practical and popular imaging technique. Compared to previous techniques, ptychography provides superior robustness and resolution at the expense of more advanced and time consuming data analysis. By taking advantage of massively parallel architectures, high-throughput processing can expedite this analysis and provide microscopists with immediate feedback. These advances allow real-time imaging at wavelength limited resolution, coupled with a large field of view. Here, we introduce a set of algorithmic and computational methodologies used at the Advanced Light Source, and DOE light sources packaged as a CUDA based software environment named SHARP (this http URL), aimed at providing state-of-the-art high-throughput ptychography reconstructions for the coming era of diffraction limited light sources.

CAMERA: The Center for Advanced Mathematics for Energy Research Applications

Advanced experimental facilities worldwide are probing structure and chemistry, disorder, dynamics and electronic properties, through time, over length scales spanning macroscopic to atomic resolution, in multiple dimensions (e.g., hyperspectral tomography, nano-spectroscopy), under extreme environmental conditions and stimulated reactions. In order to do so, they are collecting more and more data at faster and faster rates. One critical challenge is to build algorithms that can analyze, interpret, and understand the information contained within this experimental data.

Structure recognition from high resolution images of ceramic composites

Fibers provide exceptional strength-to-weight ratio capabilities when woven into ceramic composites, transforming them into materials with exceptional resistance to high temperature, and high strength combined with improved fracture toughness. Microcracks are inevitable when the material is under strain, which can be imaged using synchrotron X-ray computed micro-tomography (μ-CT) for assessment of material mechanical toughness variation. An important part of this analysis is to recognize fibrillar features. This paper presents algorithms for detecting and quantifying composite cracks and fiber breaks from high-resolution image stacks. First, we propose recognition algorithms to identify the different structures of the composite, including matrix cracks and fibers breaks. Second, we introduce our package F3D for fast filtering of large 3D imagery, implemented in OpenCL to take advantage of graphic cards. Results show that our algorithms automatically identify micro-damage and that the GPU-based implementation introduced here takes minutes, being 17x faster than similar tools on a typical image file.

Near-edge X-ray refraction fine structure microscopy

We demonstrate a method for obtaining increased spatial resolution and specificity in nanoscale chemical composition maps through the use of full refractive reference spectra in soft x-ray spectro-microscopy. Using soft x-ray ptychography, we measure both the absorption and refraction of x-rays through pristine reference materials as a function of photon energy and use these reference spectra as the basis for decomposing spatially resolved spectra from a heterogeneous sample, thereby quantifying the composition at high resolution. While conventional instruments are limited to absorption contrast, our novel refraction based method takes advantage of the strongly energy dependent scattering cross-section and can see nearly five-fold improved spatial resolution on resonance.

Real-time data-intensive computing

Today users visit synchrotrons as sources of understanding and discovery—not as sources of just light, and not as sources of data. To achieve this, the synchrotron facilities frequently provide not just light but often the entire end station and increasingly, advanced computational facilities that can reduce terabytes of data into a form that can reveal a new key insight. The Advanced Light Source (ALS) has partnered with high performance computing, fast networking, and applied mathematics groups to create a “super-facility”, giving users simultaneous access to the experimental, computational, and algorithmic resources to make this possible. This combination forms an efficient closed loop, where data—despite its high rate and volume—is transferred and processed immediately and automatically on appropriate computing resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experim...

Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics

Three-dimensional (3D) micro-tomography (µ-CT) has proven to be an important imaging modality in industry and scientific domains. Understanding the properties of material structure and behavior has produced many scientific advances. An important component of the 3D µ-CT pipeline is image partitioning (or image segmentation), a step that is used to separate various phases or components in an image. Image partitioning schemes require specific rules for different scientific fields, but a common strategy consists of devising metrics to quantify performance and accuracy. The present article proposes a set of protocols to systematically analyze and compare the results of unsupervised classification methods used for segmentation of synchrotron-based data. The proposed dataflow for Materials Segmentation and Metrics (MSM) provides 3D micro-tomography image segmentation algorithms, such as statistical region merging (SRM), k-means algorithm and parallel Markov random field (PMRF), while offering different metrics to evaluate segmentation quality, confidence and conformity with standards. Both experimental and synthetic data are assessed, illustrating quantitative results through the MSM dashboard, which can return sample information such as media porosity and permeability. The main contributions of this work are: (i) to deliver tools to improve material design and quality control; (ii) to provide datasets for benchmarking and reproducibility; (iii) to yield good practices in the absence of standards or ground-truth for ceramic composite analysis.

Maximal clique enumeration with data-parallel primitives

The enumeration of all maximal cliques in an undirected graph is a fundamental problem arising in several research areas. We consider maximal clique enumeration on shared-memory, multi-core architectures and introduce an approach consisting entirely of data-parallel operations, in an effort to achieve efficient and portable performance across different architectures. We study the performance of the algorithm via experiments varying over benchmark graphs and architectures. Overall, we observe that our algorithm achieves up to a 33-time speedup and 9-time speedup over state-of-the-art distributed and serial algorithms, respectively, for graphs with higher ratios of maximal cliques to total cliques. Further, we attain additional speedups on a GPU architecture, demonstrating the portable performance of our data-parallel design.

Nanosurveyor : a framework for real-time data processing

BackgroundThe ever improving brightness of accelerator based sources is enabling novel observations and discoveries with faster frame rates, larger fields of view, higher resolution, and higher dimensionality.ResultsHere we present an integrated software/algorithmic framework designed to capitalize on high-throughput experiments through efficient kernels, load-balanced workflows, which are scalable in design. We describe the streamlined processing pipeline of ptychography data analysis.ConclusionsThe pipeline provides throughput, compression, and resolution as well as rapid feedback to the microscope operators.

Machine learning for micro-tomography

Machine learning has revolutionized a number of fields, but many micro-tomography users have never used it for their work. The micro-tomography beamline at the Advanced Light Source (ALS), in collaboration with the Center for Applied Mathematics for Energy Research Applications (CAMERA) at Lawrence Berkeley National Laboratory, has now deployed a series of tools to automate data processing for ALS users using machine learning. This includes new reconstruction algorithms, feature extraction tools, and image classification and recommen- dation systems for scientific image. Some of these tools are either in automated pipelines that operate on data as it is collected or as stand-alone software. Others are deployed on computing resources at Berkeley Lab–from workstations to supercomputers–and made accessible to users through either scripting or easy-to-use graphical interfaces. This paper presents a progress report on this work.

Reduced-complexity image segmentation under parallel Markov Random Field formulation using graph partitioning

Markov Random Field (MRF) algorithms are powerful tools in image analysis to explore contextual information of data. However, the application of these methods to large data means that alternative approaches must be found to circumvent the NP-hard complexity of the MRF optimization. We introduce a MRF-based framework that overcomes this issue by using graph partitioning. The computational complexity is decreased as the optimization/parameter estimation is executed on small subgraphs. PMRF targets 3D microCT datasets, but we include evaluation on the Berkeley Segmentation Dataset (ranking 7th place) to fully compare our method with well-known segmentation algorithms. Segmentation results on the microCT datasets achieve precision higher than 95%.