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Building on its capabilities in data-intensive computing and computational science, Brookhaven National Laboratory is embarking upon a major new Computational Science Initiative.

Advances in computational science, data management and analysis have been a key factor in the success of Brookhaven Lab's scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source (NSLS), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, as well as our collaborative participation in international research endeavors, such as the ATLAS experiment at Europe's Large Hadron Collider.

The Computational Science Initiative (CSI) brings together under one umbrella the expertise that has driven this success to foster cross-disciplinary collaborations to address the next generation of scientific challenges posed by facilities such as the new National Synchrotron Light Source II (NSLS II). A particular focus of CSI's work will be the research, development and deployment of novel methods and algorithms for the timely analysis and interpretation of high volume, high velocity, heterogeneous scientific data created by experimental, observational and computational facilities to accelerate and advance scientific discovery. CSI is hereby taking an integrated approach, providing capabilities from leading edge research to multi-disciplinary teams that deliver operational data analysis capabilities to the scientific user communities.

Enabling Capabilities

Computer Science and Mathematics—fundamental research into novel methods and algorithm in support of large-scale, multi-modal, and streaming data analysis. Novel solutions for long term data curation and active reuse. Approaches to enable energy efficient, extreme-scale numerical modeling specifically in computational materials science, chemistry, lattice quantum chromo dynamics and fusion.

The BNL Scientific Data and Computing Center, housing the latest systems in high-performance and data-intensive computing, data storage, and networking, offering everything from novel research platforms to highly reliable production services.

Translational Capabilities

The Computational Science Laboratory, a collaborative space for the development of advanced algorithms and their characterization and optimization, also brings together computer scientists, mathematicians, and leading computational scientists to develop next-generation numerical simulation models

The Center for Data Driven Discovery (C3D), a multi-disciplinary center for the development, deployment, and operation of data-intensive discovery services for science, national security, and industry

A Multi-disciplinary, Collaborative Approach

The CSI philosophy is a multi-disciplinary and collaborative approach to scientific research and development, with research targeted at and informed by the key challenges observed in close interactions with our clients in science, national security agencies, and industry. Our success is measured in equal parts by the advancement we can bring to computer science and mathematics, as well as by the transformational impact we have on our clients’ mission space.

The CSI brings together under one umbrella the expertise that fosters cross-disciplinary collaboration and makes optimal use of existing technologies, while also leading the development of new tools and methods that will benefit science both within and beyond the Laboratory. Key partners include nearby universities such as Columbia, Cornell, New York University, Stony Brook, and Yale, as well as IBM Research.

Strategic Partnerships

Computational scientists at Brookhaven will also seek to establish partnerships with key players in academia and industry (e.g. Stony Brook University’s Institute for Advanced Computational Science, Rensselaer Polytechnic Institute, Oak Ridge National Laboratory, IBM, and Intel). One existing example of a successful partnership is the collaboration of Brookhaven Lab’s high-energy and nuclear physics research groups with IBM that led to the development of the BlueGene supercomputing architecture now used on the world’s most powerful commercially available supercomputers.

More about strategic partnerships CSI All Hands Meeting 2016

Computational Science Initiative first all-hands meeting.

  1. JAN

    20

    Today

    Computational Science Center Seminar

    "Enabling Re-executable Workflows with Near-real-time Visualization, Provenance Capture and Advanced Querying for Mass Spectrometry Data"

    Presented by Mathew Thomas, Pacific Northwest National Laboratory

    11 am, Seminar Room, Bldg. 725

    Friday, January 20, 2017, 11:00 am

    Hosted by: 'Kerstin Kleese van Dam'

    The current analysis pipeline for Nano-DESI Mass Spectral Imaging involves analyzing the data coming off the instrument using an in-house tool called MSI QuickView and saving the results onto a storage drive before moving on to the next experiment. While this works well for single datasets, there is a demand for more scalable, flexible workflows that are re-executable across datasets, support extensive querying and ease collaboration. His talk will focus on a workflow that moves data analysis from a mere desktop application for single experiments to a more general capability that can be possibly extended to perform multi-modal analysis across datasets. The core components of the workflow include (1) MSI QuickView, a desktop application for the near-real time visualization and analysis of mass spectrometry data; (2) Provenance Environment (ProvEn), a provenance production and collection framework that provides components supporting the production and collection of provenance information for distributed application environments; (3) Elasticsearch, a readily-scalable, broadly-distributable, enterprise-grade search engine that is accessible through an elaborate and extensive API to power extremely fast indexing and searches that support your data discovery applications; (4) Logstash, processing of log files; and (5) Kibana, a platform to visualize, analyze and explore data from multiple sources including Elasticsearch and Logstash.