Tags: high performance computing (HPC)

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  1. Rohit Goswami


  2. Perspectives on High-Performance Computing in a Big Data World: Part D - Learning Model Details and Agent-Based Simulations

    17 Oct 2019 | | Contributor(s):: Fox, Geoffrey C.

    This lecture completes the discussion of MLforHPC. It covers Learning Model Details and Agents and Time-Series Case Studies.

  3. Perspectives on High-Performance Computing in a Big Data World: Part E - Challenges and Opportunities, Conclusions

    17 Oct 2019 | | Contributor(s):: Fox, Geoffrey C.

    This lecture covers the computer science issues raised in this talk. The conclusions note that HPDC/HPC is essential; it is good to work closely with industry with student Internships and Collaborations; the Global AI and Modeling Supercomputer GAIMSC is a good framework with an HPC Cloud linked...

  4. Perspectives on High-Performance Computing in a Big Data World: Part C - MLaroundHPDC/HPC and MLAutotuning

    10 Oct 2019 | | Contributor(s):: Fox, Geoffrey C.

    This is the first part of the discussion of MLforHPC. It includes MLAutotuning (Using ML to configure or autotune ML or HPC simulations and MLaroundHPC (Learning outputs from inputs).

  5. Perspectives on High-Performance Computing in a Big Data World

    30 Sep 2019 | | Contributor(s):: Fox, Geoffrey C.

    This course was deleivered at ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC).High-Performance Computing (HPC) and Cyberinfrastructure have played a leadership role in computational science even since the start of the NSF computing centers program. Thirty...

  6. Perspectives on High-Performance Computing in a Big Data World: Part B - More on the Evolution of Interests and Communities

    30 Sep 2019 | | Contributor(s):: Fox, Geoffrey C.

    This part contains several topics. It discusses the importance of industry in several facets of the field: SysML conference, clouds, MLPerf, the Global AI Supercomputer. The nature of data science and data engineering jobs. We emphasize the need for HPC. We finish by introducing MLforHPC (AI for...

  7. Perspectives on High-Performance Computing in a Big Data World: Part A - Data on the Evolution of Interests and Communities

    13 Aug 2019 | | Contributor(s):: Fox, Geoffrey C.

    This lecture has an overall outline of the 5 part presentation. It covers trends seen from conferences and journals -- the number of papers, attendees and h5index. Then we look at relevant Google Trends. Cyberinfrastructure related activities are less buoyant than those for AI and ML.

  8. How do we solve big science problems using all the modern tools and technologies at our fingertips?

    06 Jul 2019 | | Contributor(s):: Jeffrey A. Nichols

    Exascale is on the horizon and ORNL just announced our next new system called Frontier to be delivered in 2021 – another order of magnitude more powerful! I will discuss the technologies used in Summit and Frontier.

  9. Myron DSilva


  10. Darren K Adams


  11. Muhammad Bilal

    Bilal’s research focuses on data-driven solutions for the environmental and health impact assessment of engineered nanomaterials (ENMs) using advanced machine learning/data mining and simulation...


  12. Non-Blocking Conjugate Gradient Methods for Extreme Scale Computing

    07 Feb 2016 | | Contributor(s):: Paul Eller

    Many scientific and engineering applications use Krylov subspace methods to solve large systems of linear equations. For extreme scale parallel computing systems, the dot products in these methods (implemented using allreduce operations in MPI) can limit performance because they are a...

  13. Range Decomposition: A Low Communication Algorithm for Solving PDEs on Massively Parallel Machines

    07 Feb 2016 | | Contributor(s):: Tom Manteuffel

    The Range Decomposition (RD) algorithm uses nested iteration and adaptive mesh refinement locally before performing a global communication step. Only several such steps are observed to be necessary before reaching a solution within a small multiple of discretization error. The target application...

  14. A Massively Parallel Semicoarsening Multigrid for 3D Reservoir Simulation on Multi-core and Multi-GPU Architectures

    04 Feb 2016 | | Contributor(s):: Abdulrahman Manea

    In this work, we have designed and implemented a massively parallel version of the Semicoarsening Black Box Multigrid Solver [1], which is capable of handling highly heterogeneous and anisotropic 3D reservoirs, on a parallel architecture with multiple GPU’s. For comparison purposes, the...

  15. A Performance Comparison of Algebraic Multigrid Preconditioners on GPUs and MIC

    04 Feb 2016 | | Contributor(s):: Karl Rupp

    Algebraic multigrid (AMG) preconditioners for accelerators such as graphics processing units (GPUs) and Intel's many-integrated core (MIC) architecture typically require a careful, problem-dependent trade-off between efficient hardware use, robustness, and convergence rate in order to...

  16. HPGMG: Benchmarking Computers Using Multigrid

    04 Feb 2016 | | Contributor(s):: Jed Brown

    HPGMG (https://hpgmg.org) is a geometric multigrid benchmark designed to measure the performance and versatility of computers. For a benchmark to be representative of applications, good performance on the benchmark should be sufficient to ensure good performance on most important applications and...

  17. Understanding the Propagation of Silent Data Corruption in Algebraic Multigrid

    04 Feb 2016 | | Contributor(s):: Jon Calhoun

    Sparse linear solvers from a fundamental kernel in high performance computing (HPC). Exascale systems are expected to be more complex than systems of today being composed of thousands of heterogeneous processing elements that operate at near-threshold-voltage to meet power constraints. The...

  18. Preparing for the Future of Computing: The DOE/ASCR Materials Co-Design Center

    08 Apr 2015 | | Contributor(s):: Jim Belak

    The advent of Advanced / Additive Manufacturing and the Materials Genome Initiative has placed significant emphasis on accelerating the qualification of new materials for use in real applications. Within these workflows lies both the engineering scale qualification through building and testing...

  19. Brenden William Hamilton

    PhD student in Materials Engineering at Purdue with a focus in shock response of materials and energetic materials. Current works include energy localization and transport, as well as...


  20. Sakhraoui Taoufik