Synchrotron light sources produce intense photon beams ranging in energy from the infrared to the hard x-ray end of the electromagnetic spectrum. NSLS-II is a new state-of-the-art, medium-energy electron storage ring designed to deliver world-leading intensity and brightness, and will produce x-rays more than 10,000 times brighter than the current NSLS at Brookhaven Lab. The photon beams at NSLS-II will be used by thousands of researchers annually in many important scientific and technology areas such as condensed matter physics, materials science, environmental science, chemistry, energy science, communications, semiconductors, catalysis, nanoscience, geo-science, biology, and membrane science. When NSLS-II begins operations in 2015, the superlative character and combination of capabilities will also have broad impact on a number of scientific initiatives, including the National Institutes of Health’s structural genomics initiative, DOE’s Genomics:GTL initiative, and the federal nanoscience initiative.
The improved brightness at NSLS-II together with the development of new detector technology requires new computing approaches to experiment control, data management, data analysis workflow, and metadata handling with high performance on data storage and retrieval, as well as visualization. These types of systems are being incorporated to provide high levels of precision control of the accelerator used to produce the beams and the beamlines at which light is used to collect experimental data. Computational architectures installed at NSLS-II experimental end stations will enable nearly real-time (in-situ) streaming analysis to reduce raw data, perform data transformations, reconstruction, analysis, and visualization. These systems will assure data quality control and inform the investigators to guide the experiment progression. To reduce the time required for configuring these tasks, the NSLS-II team has developed a prototype workflow system to help users easily define data-processing tasks via a simple web interface, to automate task management, to ensure task inter-dependency, and accelerate each task with state-of-the-art parallel computing paradigms