相关论文: Introducing APERTURE: A GPU-based General Relativi…
In large scale cosmological hydrodynamic simulations simplified sub-grid models for gas accretion onto black holes and AGN feedback are commonly used. Such models typically depend on various free parameters, which are not well constrained.…
(Abridged) We study stability of gas accretion in Active Galactic Nuclei. Our grid based simulations cover a radial range from 0.1 to 200 pc. Here, as in previous studies by our group, we include gas radiative cooling as well as heating by…
Recently, a fully implicit, energy- and charge-conserving particle-in-cell method has been proposed for multi-scale, full-f kinetic simulations [G. Chen, et al., J. Comput. Phys. 230,18 (2011)]. The method employs a Jacobian-free…
Accurate modeling of active galactic nucleus (AGN) feedback, especially due to relativistic jets, is crucial for understanding the cool-core problem in galaxy clusters. We present a new subgrid method to model accretion onto and feedback…
Approximate nearest neighbor (ANN) search in high dimensions is an integral part of several computer vision systems and gains importance in deep learning with explicit memory representations. Since PQT, FAISS, and SONG started to leverage…
The large galactic scales are connected to the many orders of magnitude smaller supermassive black hole (SMBH) scales by an episodic cycle of feeding and feedback. Active galactic nuclei (AGN) are powered by accretion onto SMBH and the…
The development and the implementation of a Particle-in-Cell code written in the Unified Parallel C (UPC) language for plasma simulations with application to astrophysics and fusion nuclear energy machines are presented. A simple one…
We describe the GPU implementation of shifted or multimass iterative solvers for sparse linear systems of the sort encountered in lattice gauge theory. We provide a generic tool that can be used by those without GPU programming experience…
Accelerated computing is widely used in high-performance computing. Therefore, it is crucial to experiment and discover how to better utilize GPUGPUs latest generations on relevant applications. In this paper, we present results and share…
Since the first idea of using GPU to general purpose computing, things have evolved over the years and now there are several approaches to GPU programming. GPU computing practically began with the introduction of CUDA (Compute Unified…
This paper presents a spectral element finite element scheme that efficiently solves elliptic problems on unstructured hexahedral meshes. The discrete equations are solved using a matrix-free preconditioned conjugate gradient algorithm. An…
Active Galactic Nuclei (AGN) are highly energetic astrophysical sources powered by accretion onto supermassive black holes in galaxies, which present unique observational signatures covering the full electromagnetic spectrum (and more) over…
Graph neural networks (GNNs) are powerful tools for exploring and learning from graph structures and features. As such, achieving high-performance execution for GNNs becomes crucially important. Prior works have proposed to explore the…
Graphic Processing Units (GPUs) have become ubiquitous in scientific computing. However, writing efficient GPU kernels can be challenging due to the need for careful code tuning. To automatically explore the kernel optimization space,…
This paper presents a GPU-accelerated simulation package, TRED, for next-generation neutrino detectors with pixelated charge readout, leveraging community-driven software ecosystems to ensure sustainability and extensibility. We introduce…
Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics. In particular, particle tracking data is…
A recent approach to simulating localized feedback from active galactic nuclei by Power et al. (2011) uses an accretion disc particle to represent both the black hole and its accretion disc. We have extrapolated and adapted this approach to…
Loopy Belief Propagation (LBP) is a widely used approximate inference algorithm in probabilistic graphical models, with applications in computer vision, error correction codes, protein folding, program analysis, etc. However, LBP faces…
Graph-based Point Cloud Networks (PCNs) are powerful tools for processing sparse sensor data with irregular geometries, as found in high-energy physics detectors. However, deploying models in such environments remains challenging due to…
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was…