41,981 research outputs found
Generating optimized Fourier interpolation routines for density function theory using SPIRAL
© 2015 IEEE.Upsampling of a multi-dimensional data-set is an operation with wide application in image processing and quantum mechanical calculations using density functional theory. For small up sampling factors as seen in the quantum chemistry code ONETEP, a time-shift based implementation that shifts samples by a fraction of the original grid spacing to fill in the intermediate values using a frequency domain Fourier property can be a good choice. Readily available highly optimized multidimensional FFT implementations are leveraged at the expense of extra passes through the entire working set. In this paper we present an optimized variant of the time-shift based up sampling. Since ONETEP handles threading, we address the memory hierarchy and SIMD vectorization, and focus on problem dimensions relevant for ONETEP. We present a formalization of this operation within the SPIRAL framework and demonstrate auto-generated and auto-tuned interpolation libraries. We compare the performance of our generated code against the previous best implementations using highly optimized FFT libraries (FFTW and MKL). We demonstrate speed-ups in isolation averaging 3x and within ONETEP of up to 15%
Overview of Parallel Platforms for Common High Performance Computing
The paper deals with various parallel platforms used for high performance computing in the signal processing domain. More precisely, the methods exploiting the multicores central processing units such as message passing interface and OpenMP are taken into account. The properties of the programming methods are experimentally proved in the application of a fast Fourier transform and a discrete cosine transform and they are compared with the possibilities of MATLAB's built-in functions and Texas Instruments digital signal processors with very long instruction word architectures. New FFT and DCT implementations were proposed and tested. The implementation phase was compared with CPU based computing methods and with possibilities of the Texas Instruments digital signal processing library on C6747 floating-point DSPs. The optimal combination of computing methods in the signal processing domain and new, fast routines' implementation is proposed as well
Laser-plasma interactions with a Fourier-Bessel Particle-in-Cell method
A new spectral particle-in-cell (PIC) method for plasma modeling is presented
and discussed. In the proposed scheme, the Fourier-Bessel transform is used to
translate the Maxwell equations to the quasi-cylindrical spectral domain. In
this domain, the equations are solved analytically in time, and the spatial
derivatives are approximated with high accuracy. In contrast to the
finite-difference time domain (FDTD) methods that are commonly used in PIC, the
developed method does not produce numerical dispersion, and does not involve
grid staggering for the electric and magnetic fields. These features are
especially valuable in modeling the wakefield acceleration of particles in
plasmas. The proposed algorithm is implemented in the code PLARES-PIC, and the
test simulations of laser plasma interactions are compared to the ones done
with the quasi-cylindrical FDTD PIC code CALDER-CIRC.Comment: submitted to Phys. Plasma
Code generator matrices as RNG conditioners
We quantify precisely the distribution of the output of a binary random
number generator (RNG) after conditioning with a binary linear code generator
matrix by showing the connection between the Walsh spectrum of the resulting
random variable and the weight distribution of the code. Previously known
bounds on the performance of linear binary codes as entropy extractors can be
derived by considering generator matrices as a selector of a subset of that
spectrum. We also extend this framework to the case of non-binary codes
A statistical model for in vivo neuronal dynamics
Single neuron models have a long tradition in computational neuroscience.
Detailed biophysical models such as the Hodgkin-Huxley model as well as
simplified neuron models such as the class of integrate-and-fire models relate
the input current to the membrane potential of the neuron. Those types of
models have been extensively fitted to in vitro data where the input current is
controlled. Those models are however of little use when it comes to
characterize intracellular in vivo recordings since the input to the neuron is
not known. Here we propose a novel single neuron model that characterizes the
statistical properties of in vivo recordings. More specifically, we propose a
stochastic process where the subthreshold membrane potential follows a Gaussian
process and the spike emission intensity depends nonlinearly on the membrane
potential as well as the spiking history. We first show that the model has a
rich dynamical repertoire since it can capture arbitrary subthreshold
autocovariance functions, firing-rate adaptations as well as arbitrary shapes
of the action potential. We then show that this model can be efficiently fitted
to data without overfitting. Finally, we show that this model can be used to
characterize and therefore precisely compare various intracellular in vivo
recordings from different animals and experimental conditions.Comment: 31 pages, 10 figure
Bayesian power-spectrum inference for Large Scale Structure data
We describe an exact, flexible, and computationally efficient algorithm for a
joint estimation of the large-scale structure and its power-spectrum, building
on a Gibbs sampling framework and present its implementation ARES (Algorithm
for REconstruction and Sampling). ARES is designed to reconstruct the 3D
power-spectrum together with the underlying dark matter density field in a
Bayesian framework, under the reasonable assumption that the long wavelength
Fourier components are Gaussian distributed. As a result ARES does not only
provide a single estimate but samples from the joint posterior of the
power-spectrum and density field conditional on a set of observations. This
enables us to calculate any desired statistical summary, in particular we are
able to provide joint uncertainty estimates. We apply our method to mock
catalogs, with highly structured observational masks and selection functions,
in order to demonstrate its ability to reconstruct the power-spectrum from real
data sets, while fully accounting for any mask induced mode coupling.Comment: 25 pages, 15 figure
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