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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…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-04-18 Lorenc Kapllani , Long Teng

Mixed-precision algorithms have been proposed as a way for scientific computing to benefit from some of the gains seen for artificial intelligence (AI) on recent high performance computing (HPC) platforms. A few applications dominated by…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-07-16 Aditya Kashi , Nicholson Koukpaizan , Hao Lu , Michael Matheson , Sarp Oral , Feiyi Wang

The recent evolution of software and hardware technologies is leading to a renewed computational interest in Particle-In-Cell (PIC) methods such as the Material Point Method (MPM). Indeed, provided some critical aspects are properly…

Computational Physics · Physics 2024-12-11 Paolo Joseph Baioni , Tommaso Benacchio , Luigi Capone , Carlo de Falco

Implicit methods and GPU parallelization are two distinct yet powerful strategies for accelerating high-order CFD algorithms. However, few studies have successfully integrated both approaches within high-speed flow solvers. The core…

Numerical Analysis · Mathematics 2025-09-09 Hongyu Liu , Xing Ji , Yuan Fu , Kun Xu

The Graphics Processing Unit (GPU) is a powerful tool for parallel computing. In the past years the performance and capabilities of GPUs have increased, and the Compute Unified Device Architecture (CUDA) - a parallel computing architecture…

Computational Physics · Physics 2009-12-17 Ferenc Molnar , Tamas Szakaly , Robert Meszaros , Istvan Lagzi

Particle-in-Cell (PIC) Monte Carlo (MC) simulations are central to plasma physics but face increasing challenges on heterogeneous HPC systems due to excessive data movement, synchronization overheads, and inefficient utilization of multiple…

We summarize a series of numerical experiments of collisional dynamics in dense stellar systems such as globular clusters (GCs) and in weakly collisional plasmas using a novel simulation technique, the so-called Multi-particle collision…

We present the implementation and performance of a class of directionally unsplit Riemann-solver-based hydrodynamic schemes on Graphic Processing Units (GPU). These schemes, including the MUSCL-Hancock method, a variant of the MUSCL-Hancock…

Instrumentation and Methods for Astrophysics · Physics 2011-09-05 Hsi-Yu Schive , Ui-Han Zhang , Tzihong Chiueh

Path integral Monte Carlo (PIMC) and path integral molecular dynamics (PIMD) provide the golden standard for the ab initio simulations of identical particles. In this work, we achieved significant GPU acceleration based on PIMD, which is…

Computational Physics · Physics 2026-03-31 Yunuo Xiong

Simulations of nematohydrodynamics on graphics processing units (GPUs) are typically performed using double precision, which ensures accuracy but significantly increases computational cost. However, consumer-grade GPUs are optimized for…

The viscosity and self-diffusion constant of particle-based mesoscale hydrodynamic methods, multi-particle collision dynamics (MPC) and dissipative particle dynamics (DPD), are investigated, both with and without angular-momentum…

Soft Condensed Matter · Physics 2009-11-13 Hiroshi Noguchi , Gerhard Gompper

In this review we discuss the recent progress in the simulation of soft active matter systems and in particular the hydrodynamics of microswimmers using the method of multiparticle collision dynamics, which solves the hydrodynamic flows…

Soft Condensed Matter · Physics 2020-09-03 Andreas Zöttl

Simulations based on particle methods, such as Smoothed Particle Hydrodynamics (SPH), are known to be computationally demanding. While such methods have for long been executed in parallel on multi-core CPUs, in recent years the increasing…

Computational Engineering, Finance, and Science · Computer Science 2026-03-18 Xiangyu Hu , Alberto Guarnieri

In order to run Computational Fluid Dynamics (CFD) codes on large scale infrastructures, parallel computing has to be used because of the computational intensive nature of the problems. In this paper we investigate the ADAPT platform where…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-05-25 Imad Kissami , Christophe Cerin , Fayssal Benkhaldoun , Gilles Scarella

A high fidelity flow simulation for complex geometries for high Reynolds number ($Re$) flow is still very challenging, which requires more powerful computational capability of HPC system. However, the development of HPC with traditional CPU…

Computational Physics · Physics 2022-03-03 Chuangchao Ye , Pengjunyi Zhang , Rui Yan , Dejun Sun , Zhenhua Wan

A key scalability challenge in neural solvers for industrial-scale physics simulations is efficiently capturing both fine-grained local interactions and long-range global dependencies across millions of spatial elements. We introduce the…

Machine Learning · Computer Science 2026-03-10 Pedro M. P. Curvo , Jan-Willem van de Meent , Maksim Zhdanov

Graphics Processing Units (GPUs) are having a transformational effect on numerical lattice quantum chromodynamics (LQCD) calculations of importance in nuclear and particle physics. The QUDA library provides a package of mixed precision…

High Energy Physics - Lattice · Physics 2010-12-06 Ronald Babich , Michael A. Clark , Bálint Joó

Parallel algorithms on CPU and GPU are implemented for the Unified Gas-Kinetic Scheme and their performances are investigated and compared by a two dimensional channel flow case. The parallel CPU algorithm has a one dimensional block…

Computational Physics · Physics 2018-11-02 Jizhou Liu , Fang Q. Hu , Xiaodong Li

Fluid simulations are often performed using the incompressible Navier-Stokes equations (INSE), leading to sparse linear systems which are difficult to solve efficiently in parallel. Recently, kinetic methods based on the…

Graphics · Computer Science 2021-01-29 Yixin Chen , Wei Li , Rui Fan , Xiaopei Liu

Nonlinear Model Predictive Control (NMPC) is a state-of-the-art approach for locomotion and manipulation which leverages trajectory optimization at each control step. While the performance of this approach is computationally bounded,…

Robotics · Computer Science 2025-07-08 Emre Adabag , Miloni Atal , William Gerard , Brian Plancher
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