Center for Computing Research (CCR)

Center for Computing Research

The Center for Computing Research (CCR) at Sandia creates technology and solutions for many of our nation's most demanding national security challenges. The Center's portfolio spans the spectrum from fundamental research to state‑of‑the‑art applications. Our work includes computer system architecture (both hardware and software); enabling technology for modeling physical and engineering systems; and research in discrete mathematics, data analytics, cognitive modeling, and decision support materials.

CCR Research

Featured News

  • CCR Researcher Discusses IO500 on Next Platform TV

    CCR system software researcher Jay Lofstead appeared on the September 3rd episode of “Next Platform TV” to discuss the IO500 benchmark, including how it is used for evaluating large- scale storage systems in high-performance computing (HPC) and the future of the benc...

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    CCR Researcher Discusses IO500 on Next Platform TV

    CCR system software researcher Jay Lofstead appeared on the September 3rd episode of “Next Platform TV” to discuss the IO500 benchmark, including how it is used for evaluating large- scale storage systems in high-performance computing (HPC) and the future of the benchmark. Jay’s discussion with Nicole Hemsoth of the Next Platform starts at the 32:04 mark of the video. In the interview, Jay describes the origins of the IO500 benchmark and the desire to provide a standard method for understanding how well an HPC storage system is performing for different workloads and different storage and file system configurations. Jay also describes how the benchmark has evolved since its inception, as well as the influence of the benchmark, and the ancillary impacts of ranking IO systems. More details and the entire episode are here:

    https://www.nextplatform.com/2020/09/03/next-platform-tv-for-september-3-2020/

    Contact: Lofstead, Gerald Fredrick (Jay)
    September 2020
    2020-9390E

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  • Key Numerical Computing Algorithm Implemented on Neuromorphic Hardware

    Researchers in Sandia’s Center for Computing Research (CCR) have demonstrated using Intel’s Loihi and IBM’s TrueNorth that neuromorphic hardware can efficiently implement Monte Carlo solutions for partial differential equations....

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    Key Numerical Computing Algorithm Implemented on Neuromorphic Hardware

    Researchers in Sandia’s Center for Computing Research (CCR) have demonstrated using Intel’s Loihi and IBM’s TrueNorth that neuromorphic hardware can efficiently implement Monte Carlo solutions for partial differential equations. CCR researchers had previously hypothesized that neuromorphic chips were capable of implementing critical Monte Carlo algorithm kernels efficiently at large scales, and this study was the first to demonstrate that this approach could be used to approximate solutions to arrive at a steady-state PDE solution.  This study formalized the mathematical description of PDEs into an algorithmic form suitable for spiking neural hardware and highlighted results from implementing this spiking Monte Carlo algorithm on Sandia’s 8-chip Loihi test board and the IBM TrueNorth chip at Lawrence Livermore National Laboratory.  These results confirmed that the computational costs scale highly efficiently with model size; suggesting that spiking architectures such as Loihi and TrueNorth may be highly desirable for particle-based PDE solutions.   This work was funded by Sandia’s Laboratory Directed Research and Development (LDRD) program and the DOE Advanced Simulation and Computing (ASC) program.  The paper has been accepted to the 2020 International Conference on Neuromorphic Systems (ICONS) and is available at https://arxiv.org/abs/2005.10904

    Figure 1. Illustration of spiking algorithm to implement random walks in parallel to model diffusion on spiking neuromorphic hardware.

    Contact: Aimone, James Bradley
    July 2020
    2020-6906 S

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  • Sandia Covid-19 Medical Resource Modeling

    As part of the Department of Energy response to the novel coronavirus pandemic of 2020, Sandia personnel developed a model to predict medical resources needed, including medical practitioners (e.g. ICU nurses, physicians, respiratory therapists), fixed resources (regular or ICU beds and ventilators), and consumable resources (masks, gowns, gloves, etc....

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    Sandia Covid-19 Medical Resource Modeling

    As part of the Department of Energy response to the novel coronavirus pandemic of 2020, Sandia personnel developed a model to predict medical resources needed, including medical practitioners (e.g. ICU nurses, physicians, respiratory therapists), fixed resources (regular or ICU beds and ventilators), and consumable resources (masks, gowns, gloves, etc.)

    Researchers in Center 1400 developed a framework for performing uncertainty analysis on the resource model.  The uncertainty analysis involved sampling 26 input parameters using the Dakota software.  The sampling was performed conditional on the patient arrival streams, which were derived from epidemiology models and had a significant effect on the projected resource needs. 

    Using two of Sandia’s High Performing Computing clusters, the generated patient streams were run through the resource model for each of 3,145 counties in the United States, where each county-level run involved 100 samples per scenario. Three different social distancing scenarios were investigated. This resulted in approximately 900,000 individual runs of the medical resource model, requiring over 500 processor hours on the HPCs. The results included mean estimates per resource per county, as well as uncertainty in those estimates (e.g., variance, 5th and 95th quantile, and exceedance probabilities).   Example results are shown in Figures 1-2.   As updated patient stream projections become available from the latest epidemiology models, the analysis can be re-run quickly to provide resource projections in rapidly changing environments. 

    For more information on Sandia research related to COVID-19, please visit the COVID-19 Research website.

    Figure 1. Figure 1. Resource needs over time with a range of uncertainty
    Figure 2. Figure 2. State or county level risk indicators can be shown

    Contact: Swiler, Laura Painton
    May 2020
    2020-5925 S

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