high performance computing on graphics processing units: hgpu.org

hgpu.org » Particle-in-cell methods

Enhancing Code Portability, Problem Scale, and Storage Efficiency in Exascale Applicationsin Exascale Applications

Nigel Tan

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Tags: AMD Radeon Vega VII, ATI, Computer science, CUDA, Heterogeneous systems, HPC, nVidia, nVidia A100, nVidia DGX-A100, nVidia Quadro RTX 5000, OpenMP, OpenMPI, Particle-in-cell methods, performance portability, Tesla V100, Thesis

September 15, 2024 by hgpu

PIConGPU: Predictive Simulations of Laser-Particle Accelerators with Manycore Hardware

Axel Hübl

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Tags: CUDA, HPC, nVidia, Package, Particle-in-cell methods, Physics, Tesla P100, Thesis

July 4, 2019 by hgpu

Optimization and Evaluation of VLPL-S Particle-in-cell Code on Knights Landing

Dandi Ding, Minhua Wen, Shan Zhou, Min Chen, James Lin

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Tags: Intel Xeon Phi, MPI, OpenMP, Particle-in-cell methods, Physics, Plasma physics

November 23, 2016 by hgpu

Co-design of a particle-in-cell plasma simulation code for Intel Xeon Phi: a first look at Knights Landing

Igor Surmin, Sergey Bastrakov, Zakhar Matveev, Evgeny Efimenko, Arkady Gonoskov, Iosif Meyerov

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Tags: Computational Physics, Intel Xeon Phi, OpenMP, Particle-in-cell methods, Physics, Plasma physics

August 8, 2016 by hgpu

Particle-in-cell algorithms for plasma simulations on heterogeneous architectures

Xavier Saez Pous

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Tags: ARM, Computational Physics, Computer science, Heterogeneous systems, MPI, OpenCL, Particle-in-cell methods, Plasma physics, Thesis

July 30, 2016 by hgpu

Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond

Erik Zenker, Rene Widera, Axel Hubl, Guido Juckeland, Andreas Knupfer, Wolfgang E. Nagel, Michael Bussmann

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenMP, Package, Particle-in-cell methods, Physics, Plasma physics, Programming techniques, Tesla K80

June 14, 2016 by hgpu

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

Benjamin Worpitz

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Tags: Computer science, CUDA, Intel Xeon Phi, OpenMP, Package, Particle-in-cell methods, Performance, performance portability, Tesla K20, Tesla K80, Thesis

April 26, 2016 by hgpu

A portable platform for accelerated PIC codes and its application to GPUs using OpenACC

F. Hariri, T.M. Tran, A. Jocksch, E. Lanti, J. Progsch, P. Messmer, S. Brunner, G. Gheller, L. Villard

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Tags: Computational Physics, Heterogeneous systems, nVidia, OpenACC, Particle-in-cell methods, Physics, Plasma physics, Tesla K20

March 12, 2016 by hgpu

Challenges Adapting CUDA PIC Codes to multiple GPUs

Olav Emil Eiksund

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Tags: Benchmarking, CUDA, Electrostatics, FFT, nVidia, nVidia GeForce GTX 980, OpenMP, Particle simulation, Particle-in-cell methods, Physics, Plasma physics, Tesla K20, Thesis

December 19, 2015 by hgpu

Modern Gyrokinetic Particle-In-Cell Simulation of Fusion Plasmas on Top Supercomputers

Bei Wang, Stephane Ethier, William Tang, Khaled Ibrahim, Kamesh Madduri, Samuel Williams, Leonid Oliker

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Tags: CUDA, Heterogeneous systems, Intel Xeon Phi, Laplace and Poisson equation, nVidia, Particle-in-cell methods, Physics, Plasma physics, Poisson equation, Tesla K20

October 25, 2015 by hgpu

Performance Analysis of a Particle-in-Cell Plasma Physics Code on Homogeneous and Heterogeneous HPC Systems

Xavier Saez, Alejandro Soba, Edilberto Sanchez, Mervi Mantsinen, Jose M. Cela

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Tags: Algorithms, ARM, Heterogeneous systems, nVidia, OpenCL, Particle-in-cell methods, Physics, Plasma physics, Tesla M2090

July 29, 2015 by hgpu

Particle-in-Cell Laser-Plasma Simulation on Xeon Phi Coprocessors

I.A. Surmin, S.I. Bastrakov, E.S. Efimenko, A.A. Gonoskov, A.V. Korzhimanov, I.B. Meyerov

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Tags: Benchmarking, Computational Physics, Intel Xeon Phi, Particle-in-cell methods, Physics, Plasma physics

May 28, 2015 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Enhancing Code Portability, Problem Scale, and Storage Efficiency in Exascale Applicationsin Exascale Applications

PIConGPU: Predictive Simulations of Laser-Particle Accelerators with Manycore Hardware

Optimization and Evaluation of VLPL-S Particle-in-cell Code on Knights Landing

Co-design of a particle-in-cell plasma simulation code for Intel Xeon Phi: a first look at Knights Landing

Particle-in-cell algorithms for plasma simulations on heterogeneous architectures

Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

A portable platform for accelerated PIC codes and its application to GPUs using OpenACC

Challenges Adapting CUDA PIC Codes to multiple GPUs

Modern Gyrokinetic Particle-In-Cell Simulation of Fusion Plasmas on Top Supercomputers

Performance Analysis of a Particle-in-Cell Plasma Physics Code on Homogeneous and Heterogeneous HPC Systems

Particle-in-Cell Laser-Plasma Simulation on Xeon Phi Coprocessors

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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