On the Development of an Implicit Stabilized FE Solver for Resistive MHD with Integrated Adjoint Error-Estimation
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While it made sense for historical reasons to develop different interfaces and protocols for memory channels, CPU to CPU interactions, and I/O devices, ongoing developments in the computer industry are leading to more converged requirements and physical implementations for these interconnects. As it becomes increasingly common for advanced components to contain a variety of computational devices as well as memory, the distinction between processors, memory, accelerators, and I/O devices becomes increasingly blurred. As a result, the interface requirements among such components are converging. There is also a wide range of new disruptive technologies that will impact the computer market in the coming years, including 3D integration and emerging NVRAM memory. Optimal exploitation of these technologies cannot be done with the existing memory, storage, and I/O interface standards. The computer industry has historically made major advances when industry players have been able to add innovation behind a standard interface. The standard interface provides a large market for their products and enables relatively quick and widespread adoption. To enable a new wave of innovation in the form of advanced memory products and accelerators, we need a new standard interface explicitly designed to provide both the performance and flexibility to support new system integration solutions
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http://www.arXiv.org and Physical Review A
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Density Functional Theory (DFT) based Equation of State (EOS) construction is a prominent part of Sandia’s capabilities to support engineering sciences. This capability is based on augmenting experimental data with information gained from computational investigations, especially in those parts of the phase space where experimental data is hard, dangerous, or expensive to obtain. A key part of the success of the Sandia approach is the fundamental science work supporting the computational capability. Not only does this work enhance the capability to perform highly accurate calculations but it also provides crucial insight into the limitations of the computational tools, providing high confidence in the results even where results cannot be, or have not yet been, validated by experimental data. This report concerns the key ingredient of projector augmented-wave (PAW) potentials for use in pseudo-potential computational codes. Using the tools discussed in SAND2012-7389 we assess the standard Vienna Ab-initio Simulation Package (VASP) PAWs for Molybdenum.
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This report documents the development, demonstration and validation of a mesoscale, microstructural evolution model for simulation of zirconium hydride {delta}-ZrH{sub 1.5} precipitation in the cladding of used nuclear fuels that may occur during long-term dry storage. While the Zr-based claddings are manufactured free of any hydrogen, they absorb hydrogen during service, in the reactor by a process commonly termed ‘hydrogen pick-up’. The precipitation and growth of zirconium hydrides during dry storage is one of the most likely fuel rod integrity failure mechanisms either by embrittlement or delayed hydride cracking of the cladding. While the phenomenon is well documented and identified as a potential key failure mechanism during long-term dry storage (NUREG/CR-7116), the ability to actually predict the formation of hydrides is poor. The model being documented in this work is a computational capability for the prediction of hydride formation in different claddings of used nuclear fuels. This work supports the Used Fuel Disposition Research and Development Campaign in assessing the structural engineering performance of the cladding during and after long-term dry storage. This document demonstrates a basic hydride precipitation model that is built on a recently developed hybrid Potts-phase field model that combines elements of Potts-Monte Carlo and the phase-field models. The model capabilities are demonstrated along with the incorporation of the starting microstructure, thermodynamics of the Zr-H system and the hydride formation mechanism.
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Journal of Computational Physics
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Journal of Chemical Physics
Several extensions and improvements have been made to the multilevel summation method (MSM) of computing long-range electrostatic interactions. These include pressure calculation, an improved error estimator, faster direct part calculation, extension to non-orthogonal (triclinic) systems, and parallelization using the domain decomposition method. MSM also allows fully non-periodic long-range electrostatics calculations which are not possible using traditional Ewald-based methods. In spite of these significant improvements to the MSM algorithm, the particle-particle particle-mesh (PPPM) method was still found to be faster for the periodic systems we tested on a single processor. However, the fast Fourier transforms (FFTs) that PPPM relies on represent a major scaling bottleneck for the method when running on many cores (because the many-to-many communication pattern of the FFT becomes expensive) and MSM scales better than PPPM when using a large core count for two test problems on Sandia's Redsky machine. This FFT bottleneck can be reduced by running PPPM on only a subset of the total processors. MSM is most competitive for relatively low accuracy calculations. On Sandia's Chama machine, however, PPPM is found to scale better than MSM for all core counts that we tested. These results suggest that PPPM is usually more efficient than MSM for typical problems running on current high performance computers. However, further improvements to MSM algorithm could increase its competitiveness for calculation of long-range electrostatic interactions. © 2014 AIP Publishing LLC.
Recovery from process loss during the execution of a distributed memory parallel application is presently achieved by restarting the program, typically from a checkpoint file. Future computer system trends indicate that the size of data to checkpoint, the lack of improvement in parallel file system performance and the increase in process failure rates will lead to situations where checkpoint restart becomes infeasible. In this report we describe and prototype the use of a new application level resilient computing model that manages persistent storage of local state for each process such that, if a process fails, recovery can be performed locally without requiring access to a global checkpoint file. LFLR provides application developers with an ability to recover locally and continue application execution when a process is lost. This report discusses what features are required from the hardware, OS and runtime layers, and what approaches application developers might use in the design of future codes, including a demonstration of LFLR-enabled MiniFE code from the Matenvo mini-application suite.
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