Sandia researchers in the Center for Computing Research collaborated with engineers from Red Hat, the world’s leading provider of open source solutions for enterprise computing, to enable more robust production container capabilities for high-performance computing (HPC) systems. CCR researchers demonstrated the use of Podman, which allows ordinary users to build and run containers without needing the elevated security privileges of an administrator, on the Stria machine at Sandia. Stria is an unclassified version of Astra, which was the first petascale HPC system based on an Arm processor. While Arm processors have shown to be very capable for HPC workloads, they are not as prevalent in laptops and workstations as other processors. To address this limitation, Podman provides the ability to build containers directly on machines like Stria and Astra without requiring root-level access. This capability is a critical advancement in container functionality for the HPC application development environment. The CCR team is continuing to work with Red Hat on improving Podman for traditional HPC applications as well as machine learning and deep learning workloads. More details on this collaboration can be found here:

Contact: Younge, Andrew J
July 2020
2020-6891E

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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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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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