Publications (6)
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Amrita Mathuriya, Deborah Bard, Peter Mendygral +14
Deep learning is a promising tool to determine the physical model that describes our universe. To handle the considerable computational cost of this problem, we present CosmoFlow:…
Optimization and parallelization of B-spline based orbital evaluations in QMC on multi/many-core shared memory processors
Amrita Mathuriya, Ye Luo, Anouar Benali +2
B-spline based orbital representations are widely used in Quantum Monte Carlo (QMC) simulations of solids, historically taking as much as 50% of the total run time. Random accesses…
Solid-state transcapacitor, a new gain element for logic, memory and interconnects
Amrita Mathuriya, Roza Kotlyar, Neal Reynolds +9
Today's transistors dictate the voltage and charge scales for both logic and memory. While AI systems are recognized to be limited by memory energy, the dominant share of the energ…
Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer
Amrita Mathuriya, Thorsten Kurth, Vivek Rane +5
We explore scaling of the standard distributed Tensorflow with GRPC primitives on up to 512 Intel Xeon Phi (KNL) nodes of Cori supercomputer with synchronous stochastic gradient de…
Embracing a new era of highly efficient and productive quantum Monte Carlo simulations
Amrita Mathuriya, Ye Luo, Raymond C. Clay +3
QMCPACK has enabled cutting-edge materials research on supercomputers for over a decade. It scales nearly ideally but has low single-node efficiency due to the physics-based abstra…
QMCPACK : An open source ab initio Quantum Monte Carlo package for the electronic structure of atoms, molecules, and solids
Jeongnim Kim, Andrew Baczewski, Todd D. Beaudet +45
QMCPACK is an open source quantum Monte Carlo package for ab-initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms,…