38 research outputs found
Many-body interactions in quasi-freestanding graphene
The Landau-Fermi liquid picture for quasiparticles assumes that charge
carriers are dressed by many-body interactions, forming one of the fundamental
theories of solids. Whether this picture still holds for a semimetal like
graphene at the neutrality point, i.e., when the chemical potential coincides
with the Dirac point energy, is one of the long-standing puzzles in this field.
Here we present such a study in quasi-freestanding graphene by using
high-resolution angle-resolved photoemission spectroscopy. We see the
electron-electron and electron-phonon interactions go through substantial
changes when the semimetallic regime is approached, including renormalizations
due to strong electron-electron interactions with similarities to marginal
Fermi liquid behavior. These findings set a new benchmark in our understanding
of many-body physics in graphene and a variety of novel materials with Dirac
fermions.Comment: PNAS 2011 ; published ahead of print June 27, 201
Coulomb-hole summations and energies for GW calculations with limited number of empty orbitals: a modified static remainder approach
Ab initio GW calculations are a standard method for computing the
spectroscopic properties of many materials. The most computationally expensive
part in conventional implementations of the method is the generation and
summation over the large number of empty orbitals required to converge the
electron self energy. We propose a scheme to reduce the summation over empty
states by the use of a modified static-remainder approximation, which is simple
to implement and yields accurate self energies for both bulk and molecular
systems requiring a small fraction of the typical number of empty orbitals
Exascale Deep Learning for Climate Analytics
We extract pixel-level masks of extreme weather patterns using variants of
Tiramisu and DeepLabv3+ neural networks. We describe improvements to the
software frameworks, input pipeline, and the network training algorithms
necessary to efficiently scale deep learning on the Piz Daint and Summit
systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained
throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up
to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel
efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor
Cores, a half-precision version of the DeepLabv3+ network achieves a peak and
sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.Comment: 12 pages, 5 tables, 4, figures, Super Computing Conference November
11-16, 2018, Dallas, TX, US
Galactos: Computing the Anisotropic 3-Point Correlation Function for 2 Billion Galaxies
The nature of dark energy and the complete theory of gravity are two central
questions currently facing cosmology. A vital tool for addressing them is the
3-point correlation function (3PCF), which probes deviations from a spatially
random distribution of galaxies. However, the 3PCF's formidable computational
expense has prevented its application to astronomical surveys comprising
millions to billions of galaxies. We present Galactos, a high-performance
implementation of a novel, O(N^2) algorithm that uses a load-balanced k-d tree
and spherical harmonic expansions to compute the anisotropic 3PCF. Our
implementation is optimized for the Intel Xeon Phi architecture, exploiting
SIMD parallelism, instruction and thread concurrency, and significant L1 and L2
cache reuse, reaching 39% of peak performance on a single node. Galactos scales
to the full Cori system, achieving 9.8PF (peak) and 5.06PF (sustained) across
9636 nodes, making the 3PCF easily computable for all galaxies in the
observable universe.Comment: 11 pages, 7 figures, accepted to SuperComputing 201