Related papers: Accelerating Dust Temperature Calculations with Gr…

Dust temperature is an important property of the interstellar medium (ISM) of galaxies. It is required when converting (sub)millimeter broadband flux to total infrared luminosity (L_IR), and hence star formation rate, in high-z galaxies.…

Stochastic heating of small grains is often mentioned as a primary cause of large infrared (IR) fluxes from star-forming galaxies, e.g. at 24\mu m. If the mechanism does work at a galaxy-wide scale, it should show up at smaller scales as…

The future of computation is the Graphical Processing Unit, i.e. the GPU. The promise that the graphics cards have shown in the field of image processing and accelerated rendering of 3D scenes, and the computational capability that these…

In this paper and a companion paper we present the DIRTY model, a Monte Carlo radiative transfer code, self-consistently including dust heating and emission, and accounting for the effects of the transient heating of small grains. The code…

In this study we demonstrate for the first time that the unified Monte Carlo approach can be applied to model gas-grain chemistry in large reaction networks. Specifically, we build a time-dependent gas-grain chemical model of the…

We describe how quantum Monte Carlo calculations using the CASINO software can be accelerated using graphics processing units (GPUs) and OpenACC. In particular we consider offloading Ewald summation, the evaluation of long-range two-body…

The goal of this work is to parallelize the multistep scheme for the numerical approximation of the backward stochastic differential equations (BSDEs) in order to achieve both, a high accuracy and a reduction of the computation time as…

New far-infrared and submillimeter data are used to solidify and to extend to long wavelengths the empirical calibration of the infrared spectral energy distribution (SED) of normal star-forming galaxies. As was found by Dale et al. (2001),…

In this paper, we aim to introduce a new perspective when comparing highly parallelized algorithms on GPU: the energy consumption of the GPU. We give an analysis of the performance of linear algebra operations, including addition of…

Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despite their importance for climate, they remain relatively…

Results of a case study of a sample of low-redshift galaxies are presented, to determine dust temperatures and emissivity indices through a less time-consuming method, and to connect both global and integrated galaxy properties with those…

Computing maximum/minimum distances between 3D meshes is crucial for various applications, i.e., robotics, CAD, VR/AR, etc. In this work, we introduce a highly parallel algorithm (gDist) optimized for Graphics Processing Units (GPUs), which…

This paper is part of a series investigating the observational appearance of planets accreting from their nascent protoplanetary disk (PPD). We evaluate the differences between gas temperature distributions determined in our radiation…

The latest Graphics Processing Units (GPUs) are reported to reach up to 200 billion floating point operations per second (200 Gflops) and to have price performance of 0.1 cents per M flop. These facts raise great interest in the…

We present computational performance comparisons of gas-solid simulations performed on current CPU and GPU architectures using MFiX Exa, a CFD-DEM solver that leverages hybrid CPU+GPU parallelism. A representative fluidized bed simulation…

Graph Neural Networks deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence. This is particularly problematic in high stakes applications where accurate…

The Random Phase Approximation (RPA) for correlation energy in the grid-based projector augmented wave (gpaw) code is accelerated by porting to the Graphics Processing Unit (GPU) architecture. The acceleration is achieved by grouping…

Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the…

In this paper we present two efficient implementations of the diffusion approximation to be employed in Monte Carlo computations of radiative transfer in dusty media of massive circumstellar disks. The aim is to improve the accuracy of the…

We discuss an implementation of molecular dynamics (MD) simulations on a graphic processing unit (GPU) in the NVIDIA CUDA language. We tested our code on a modern GPU, the NVIDIA GeForce 8800 GTX. Results for two MD algorithms suitable for…