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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Anisotropic thermal conductivity tensor of β-Y2Si2O7 for orientational control of heat flow on micrometer scales
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Measurements of pressure broadening of N2 in the anisotropic tensor component of spontaneous Raman spectra
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Baseline estimation of commercial building HVAC fan power using tensor completion
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Diffusion Tensor Imaging-Based Analysis of Baseline Neurocognitive Function and Posttreatment White Matter Changes in Pediatric Patients With Craniopharyngioma Treated With Surgery and Proton Therapy
To determine the preirradiation baseline association of white matter integrity with neurocognitive function and to assess posttreatment changes in pediatric patients with craniopharyngioma treated with proton therapy.
Tensor-train WENO scheme for compressible flows
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Learning hyperelastic anisotropy from data via a tensor basis neural network
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Kernel fusion in atomistic spin dynamics simulations on Nvidia GPUs using tensor core
In atomistic spin dynamics simulations, the time cost of constructing the space- and time-displaced pair correlation function in real space increases quadratically as the number of spins N, leading to significant computational effort. The GEMM subroutine can be adopted to accelerate the calculation of the dynamical spin-spin correlation function, but the computational cost of simulating large spin systems (>40000 spins) on CPUs remains expensive. In this work, we perform the simulation on the graphics processing unit (GPU), a hardware solution widely used as an accelerator for scientific computing and deep learning. Here we show that GPUs can accelerate the simulation up to 25-fold compared to multi-core CPUs when using the GEMM subroutine on both. To hide memory latency, we fuse the element-wise operation into the GEMM kernel using CUTLASS that can improve the performance by 26% ~ 33% compared to implementation based on cuBLAS. Furthermore, we perform the on-the-fly calculation in the epilogue of the GEMM subroutine to avoid saving intermediate results on global memory, which makes the large-scale atomistic spin dynamics simulation feasible and affordable.
qlty: Handling large tensors in scientific imaging deep-learning workflows
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Efficient Construction of Canonical Polyadic Approximations of Tensor Networks
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Tensor Hypercontraction Form of the Perturbative Triples Energy in Coupled-Cluster Theory
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Massively Parallel Tensor Network State Algorithms on Hybrid CPU-GPU Based Architectures
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