32,499 research outputs found
Quantum Monte Carlo for large chemical systems: Implementing efficient strategies for petascale platforms and beyond
Various strategies to implement efficiently QMC simulations for large
chemical systems are presented. These include: i.) the introduction of an
efficient algorithm to calculate the computationally expensive Slater matrices.
This novel scheme is based on the use of the highly localized character of
atomic Gaussian basis functions (not the molecular orbitals as usually done),
ii.) the possibility of keeping the memory footprint minimal, iii.) the
important enhancement of single-core performance when efficient optimization
tools are employed, and iv.) the definition of a universal, dynamic,
fault-tolerant, and load-balanced computational framework adapted to all kinds
of computational platforms (massively parallel machines, clusters, or
distributed grids). These strategies have been implemented in the QMC=Chem code
developed at Toulouse and illustrated with numerical applications on small
peptides of increasing sizes (158, 434, 1056 and 1731 electrons). Using 10k-80k
computing cores of the Curie machine (GENCI-TGCC-CEA, France) QMC=Chem has been
shown to be capable of running at the petascale level, thus demonstrating that
for this machine a large part of the peak performance can be achieved.
Implementation of large-scale QMC simulations for future exascale platforms
with a comparable level of efficiency is expected to be feasible
Fast systematic encoding of multiplicity codes
We present quasi-linear time systematic encoding algorithms for multiplicity
codes. The algorithms have their origins in the fast multivariate interpolation
and evaluation algorithms of van der Hoeven and Schost (2013), which we
generalise to address certain Hermite-type interpolation and evaluation
problems. By providing fast encoding algorithms for multiplicity codes, we
remove an obstruction on the road to the practical application of the private
information retrieval protocol of Augot, Levy-dit-Vehel and Shikfa (2014)
Curriculum Guidelines for Undergraduate Programs in Data Science
The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program
met for the purpose of composing guidelines for undergraduate programs in Data
Science. The group consisted of 25 undergraduate faculty from a variety of
institutions in the U.S., primarily from the disciplines of mathematics,
statistics and computer science. These guidelines are meant to provide some
structure for institutions planning for or revising a major in Data Science
Users Guide for SnadiOpt: A Package Adding Automatic Differentiation to Snopt
SnadiOpt is a package that supports the use of the automatic differentiation
package ADIFOR with the optimization package Snopt. Snopt is a general-purpose
system for solving optimization problems with many variables and constraints.
It minimizes a linear or nonlinear function subject to bounds on the variables
and sparse linear or nonlinear constraints. It is suitable for large-scale
linear and quadratic programming and for linearly constrained optimization, as
well as for general nonlinear programs. The method used by Snopt requires the
first derivatives of the objective and constraint functions to be available.
The SnadiOpt package allows users to avoid the time-consuming and error-prone
process of evaluating and coding these derivatives. Given Fortran code for
evaluating only the values of the objective and constraints, SnadiOpt
automatically generates the code for evaluating the derivatives and builds the
relevant Snopt input files and sparse data structures.Comment: pages i-iv, 1-2
A fast multipole method for stellar dynamics
The approximate computation of all gravitational forces between
interacting particles via the fast multipole method (FMM) can be made as
accurate as direct summation, but requires less than
operations. FMM groups particles into spatially bounded cells and uses
cell-cell interactions to approximate the force at any position within the sink
cell by a Taylor expansion obtained from the multipole expansion of the source
cell. By employing a novel estimate for the errors incurred in this process, I
minimise the computational effort required for a given accuracy and obtain a
well-behaved distribution of force errors. For relative force errors of
, the computational costs exhibit an empirical scaling of . My implementation (running on a 16 core node) out-performs a
GPU-based direct summation with comparable force errors for .Comment: 21 pages, 15 figures, accepted for publication in Journal for
Computational Astrophysics and Cosmolog
PennyLane: Automatic differentiation of hybrid quantum-classical computations
PennyLane is a Python 3 software framework for optimization and machine
learning of quantum and hybrid quantum-classical computations. The library
provides a unified architecture for near-term quantum computing devices,
supporting both qubit and continuous-variable paradigms. PennyLane's core
feature is the ability to compute gradients of variational quantum circuits in
a way that is compatible with classical techniques such as backpropagation.
PennyLane thus extends the automatic differentiation algorithms common in
optimization and machine learning to include quantum and hybrid computations. A
plugin system makes the framework compatible with any gate-based quantum
simulator or hardware. We provide plugins for Strawberry Fields, Rigetti
Forest, Qiskit, Cirq, and ProjectQ, allowing PennyLane optimizations to be run
on publicly accessible quantum devices provided by Rigetti and IBM Q. On the
classical front, PennyLane interfaces with accelerated machine learning
libraries such as TensorFlow, PyTorch, and autograd. PennyLane can be used for
the optimization of variational quantum eigensolvers, quantum approximate
optimization, quantum machine learning models, and many other applications.Comment: Code available at https://github.com/XanaduAI/pennylane/ .
Significant contributions to the code (new features, new plugins, etc.) will
be recognized by the opportunity to be a co-author on this pape
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