Orchestration of materials science workflows for heterogeneous resources at large scale

Abstract

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.

Enhancing Scientific Research with FAIR Digital Objects in the National Science Data Fabric

“…The workflow for studying these phenomena comsix key stages integrated into the NSDF: downloading images from X-ray tomography scans (each image is approximately 6 GB), segmenting the image to remove noise, reconstructing the image to obtain a clear view of the material structure, converting the data into OpenVisus formats, streaming the data to storage, and utilizing OpenVisus streaming services for user analysis. 9 Figure 4 shows a set of output images generated with the NSDF through its Jupyter Notebook interface. Specifically, the figure shows silicabased nanopillar images generated with synchrotronbased X-ray computed microtomography during a compression test.…”