Search for: All records

In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture. 

The design and implementation of new beamlines featuring side-bounce (single-reflection) diamond monochromators at Cornell High Energy Synchrotron Source (CHESS) are described. Undulator radiation is monochromated using an interchangeable set of diamond crystal plates reflecting radiation in the horizontal (synchrotron) plane, where each crystal plate is set to one of the low-index Bragg reflections (111, 220, 311 and 400) in either Bragg or Laue reflection geometries. At the nominal Bragg angle of 18° these reflections deliver monochromated X-rays with photon energies of 9.7, 15.9, 18.65 and 22.5 keV, respectively. An X-ray mirror downstream of the diamond monochromator is used for rejection of higher radiation harmonics and for initial focusing of the monochromated beam. The characteristics of the X-ray beam entering the experimental station were measured experimentally and compared with the results of simulations. A reasonable agreement is demonstrated. It is shown that the use of selected high-dislocation-density `mosaic' diamond single-crystal plates produced using the chemical vapor deposition method yields a few-fold enhancement in the flux density of the monochromated beam in comparison with that delivered by perfect crystals under the same conditions. At present, the Functional Materials Beamline at CHESS, which is used for time-resolved in situ characterization of soft materials during processing, has been outfitted with the described setup.