Calibration and Propagation of Model Form Error Across Experimental Settings
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Jump to search filtersMechanics of Materials Utilizing Machine Learning: Examples at Sandia National Laboratories
Abstract not provided.
Distributed Generalized Canonical Polyadic Decomposition
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Multifidelity modeling for uncertainty assessment
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Advanced Tri-lab Software Environment (ATSE)
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Bedrock Inversion and Hyper Differential Sensitivity Analysis for the Shallow Ice Model
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A Tiered Approach to Software Quality in National Laboratory Research Science
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Accomplishments of Sandia and Kitware CMake/CTest/CDash Contract for (FY2017-2020)
We describe the accomplishments jointly achieved by Kitware and Sandia over the fiscal years 2016 through 2020 to benefit the Advanced Scientific Computed (ASC) Advanced Technology Development and Mitigation (ATDM) project. As a result of our collaboration, we have improved the Trilinos and ATDM application developer experience by decreasing the time to build, making it easier to identify and resolve build and test defects, and addressing other issues . We have also reduced the turnaround time for continuous integration (CI) results. For example, the combined improvements likely cut the wall clock time to run automated builds of Trilinos posting to CDash by approximately 6x or more in many cases. We primarily achieved these benefits by contributing changes to the Kitware CMake/CTest/CDash suite of open source software development support tools. As a result, ASC developers can now spend more time improving code and less time chasing bugs. And, without this work, one can argue that the stabilization of Trilinos for the ATDM platforms would not have been feasible which would have had a large negative impact on an important internal FY20 L1 milestone.
Connecting Functions of Nonlinear Ordinary Differential Equations
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Exagraph: Combinatorial Methods for Enabling Exascale Science
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Using Advanced Git Workflows to Address Difficult Development and Integration Challenges
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Developing an Integrated, Interdisciplinary RSE Department at Sandia National Laboratories
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Fluid flow control devices with 3D-graded permeability
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A hybrid analytic-numerical compact model for radiation-induced photocurrent effects
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TChem ? An Open Source Computational Chemistry Software Library for Heterogeneous Computing Platforms
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Perfectly Adiabatic CMOS Logic
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Uncertainty quantification in cyber experimentation
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GP and PCE surrogate models for estimation of sensitivity indices
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Application of Multifidelity Uncertainty Quantification Towards Multi-turbine Interaction and Wake Characterization
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An Introduction to the Materials Learning Algorithms Package (MALA)
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Towards a Culture of Continuous Improvement within RSE Teams
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Assessment of Material Bond Integrity via Inversion for Thermophysical Properties
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Decomposition of Partial Erasure Channels
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Sensitivity Analysis for the Latest Crystalline Reference Case
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Extending sparse tensor accelerators to support multiple compression formats
Proceedings - 2021 IEEE 35th International Parallel and Distributed Processing Symposium, IPDPS 2021
Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential memory and compute savings. These applications use data stored in a variety of compression formats. We demonstrate that both the compactness of different compression formats and the compute efficiency of the algorithms enabled by them vary across tensor dimensions and amount of sparsity. Since DL and scientific workloads span across all sparsity regions, there can be numerous format combinations for optimizing memory and compute efficiency. Unfortunately, many proposed accelerators operate on one or two fixed format combinations. This work proposes hardware extensions to accelerators for supporting numerous format combinations seamlessly and demonstrates ∼ 4 × speedup over performing format conversions in software.
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