20 research outputs found
Accelerated Event-by-Event Neutrino Oscillation Reweighting with Matter Effects on a GPU
Oscillation probability calculations are becoming increasingly CPU intensive
in modern neutrino oscillation analyses. The independency of reweighting
individual events in a Monte Carlo sample lends itself to parallel
implementation on a Graphics Processing Unit. The library "Prob3++" was ported
to the GPU using the CUDA C API, allowing for large scale parallelized
calculations of neutrino oscillation probabilities through matter of constant
density, decreasing the execution time by a factor of 75, when compared to
performance on a single CPU.Comment: Final Update: Post submission update Updated version: quantified the
difference in event rates for binned and event-by-event reweighting with a
typical binning scheme. Improved formatting of reference
Distributed Block Coordinate Descent for Minimizing Partially Separable Functions
In this work we propose a distributed randomized block coordinate descent
method for minimizing a convex function with a huge number of
variables/coordinates. We analyze its complexity under the assumption that the
smooth part of the objective function is partially block separable, and show
that the degree of separability directly influences the complexity. This
extends the results in [Richtarik, Takac: Parallel coordinate descent methods
for big data optimization] to a distributed environment. We first show that
partially block separable functions admit an expected separable
overapproximation (ESO) with respect to a distributed sampling, compute the ESO
parameters, and then specialize complexity results from recent literature that
hold under the generic ESO assumption. We describe several approaches to
distribution and synchronization of the computation across a cluster of
multi-core computers and provide promising computational results.Comment: in Recent Developments in Numerical Analysis and Optimization, 201
An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging
In this paper we describe a method for retrospective estimation and correction of eddy current (EC)-induced distortions and subject movement in diffusion imaging. In addition a susceptibility-induced field can be supplied and will be incorporated into the calculations in a way that accurately reflects that the two fields (susceptibility- and EC-induced) behave differently in the presence of subject movement. The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b-value data where the contrast is vastly different in different volumes. In addition we show that the linear EC-model commonly used is insufficient for the data used in the present paper (high spatial and angular resolution data acquired with Stejskal–Tanner gradients on a 3 T Siemens Verio, a 3 T Siemens Connectome Skyra or a 7 T Siemens Magnetome scanner) and that a higher order model performs significantly better.
The method is already in extensive practical use and is used by four major projects (the WU-UMinn HCP, the MGH HCP, the UK Biobank and the Whitehall studies) to correct for distortions and subject movement