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 Kurt Ferreira Co-Authors Best Paper at APDCM Workshop

    CCR Researcher Kurt Ferreira and his co-authors have been awarded Best Paper at the upcoming Workshop on Advances in Parallel and Distributed Computational Models (APDCM) at the International Parallel and Distributed Processing Symposium....

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    CCR Researcher Kurt Ferreira Co-Authors Best Paper at APDCM Workshop

    CCR Researcher Kurt Ferreira and his co-authors have been awarded Best Paper at the upcoming Workshop on Advances in Parallel and Distributed Computational Models (APDCM) at the International Parallel and Distributed Processing Symposium. Their paper entitled "Optimal Cooperative Checkpointing for Shared High-Performance Computing Platforms" proposes a cooperative checkpoint scheduling policy that combines optimal checkpointing periods with I/O scheduling in an effort to ensure minimal overheads in the presence of bursty, competing I/O. This work provides crucial analysis and direct guidance on maximizing throughput on current and future extreme-scale platforms. This year marks the 20th APDCM Workshop, which intends “to provide a timely forum for the exchange and dissemination of new ideas, techniques and research in the field of the parallel and distributed computational models.”

    Contact: Ferreira, Kurt Brian
    May 2018
    2018-4849E

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  • NVIDIA has invited SNL to present results of a GPU performant shock hydrodynamics code at their Super Computing (SC17) booth.

    NVIDIA has invited SNL to present results of a GPU performant coupled hydrodynamics, low Magnetic Reynolds number  (low Rm) code at their Super Computing 17 (SC17) booth. Researchers at Sandia are developing a new shock hydrodynamics capability, based on adaptive Lagrangian techniques targeted at next generation architectures....

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    NVIDIA has invited SNL to present results of a GPU performant shock hydrodynamics code at their Super Computing (SC17) booth.

    NVIDIA has invited SNL to present results of a GPU performant coupled hydrodynamics, low Magnetic Reynolds number  (low Rm) code at their Super Computing 17 (SC17) booth. Researchers at Sandia are developing a new shock hydrodynamics capability, based on adaptive Lagrangian techniques targeted at next generation architectures. The code simulates shock hydrodynamics on GPU  architectures using the Kokkos library to provide portability across architectures.  Mesh and field data management, as well as adaptive Lagrangian operations are being developed to run exclusively on the GPU.  New algorithms using tetrahedral elements and a predictor-corrector time integrator have been implemented. Low Rm physics is solved using NVIDIA’s AmgX GPU-aware, algebraic multigrid solver. Using an exemplar problem provided by our NW partners we have demonstrated good scaling and performance on next generation architectures.  Notably, the exemplar problem demonstrates the advantages of a device-centric design philosophy, where the hydrodynamics physics solve, including adaptivity and remapping, are hosted on the coprocessor with exceptional performance on the GPU relative to traditional multi-core architectures. Additionally, solve times for the low Rm physics with the AmgX software demonstrate sub-second solve times for million degree of freedom problems. Next steps include full-scale testing on Trinity (on both the Haswell and KNL partitions) as well as Sierra as it becomes available, the addition of robust treatment for material/material interactions and the inclusion of more comprehensive MHD physics.

    Contact: Hansen, Glen
    February 2018
    2018-1506 O

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