high performance computing on graphics processing units: hgpu.org

hgpu.org » Physics

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

Fabio Baruffa, Luigi Iapichino, Nicolay J. Hammer, Vasileios Karakasis

View

Tags: Algorithms, Astrophysics, Computational Physics, Computer science, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, OpenMP, Performance, Physics

December 20, 2016 by hgpu

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Daniel Winkler, Michael Meister, Massoud Rezavand, Wolfgang Rauch

View

Tags: Algorithms, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Physics

December 20, 2016 by hgpu

Speedup for quantum optimal control from GPU-based automatic differentiation

Nelson Leung, Mohamed Abdelhafez, Jens Koch, David I. Schuster

View

Tags: Algorithms, CUDA, Deep learning, Mesoscale and Nanoscale Physics, Numerical simulation, nVidia, Package, Physics, Python, Quantum Physics, TensorFlow, Tesla K40

December 17, 2016 by hgpu

Parallel Level set algorithm with MPI and accelerated on GPU

Zhenlin Wang

View

Tags: Computational Physics, CUDA, Finite difference, MPI, nVidia, Package, PDEs, Physics

December 17, 2016 by hgpu

Brownian Dynamics of Active Sphere Suspensions Confined Near a No-Slip Boundary

Florencio Balboa Usabiaga, Blaise Delmotte, Aleksandar Donev

View

Tags: Condensed matter, CUDA, nVidia, Package, Physics, pyCUDA, Soft Condensed Matter

December 6, 2016 by hgpu

GPU-accelerated algorithms for many-particle continuous-time quantum walks

Enrico Piccinini, Claudia Benedetti, Ilaria Siloi, Matteo G. A. Paris, Paolo Bordone

View

Tags: Algorithms, Benchmarking, Computational Physics, CUDA, nVidia, nVidia GeForce GTX 980, OpenMP, Physics, Quantum computing, Quantum Physics, Quantum walks, Tesla K40, Tesla K80, Tesla M2050

December 6, 2016 by hgpu

GeantV: from CPU to accelerators

G. Amadio, A. Ananya, J. Apostolakis, A. Arora, M. Bandieramonte, A. Bhattacharyya, C. Bianchini, R. Brun, P. Canal, F. Carminati, L. Duhem, D. Elvira, A. Gheata, M. Gheata, I. Goulas, R. Iope, S. Jun, G. Lima, A. Mohanty, T. Nikitina, M. Novak, W. Pokorski, A. Ribon, R. Sehgal, O. Shadura, S. Vallecorsa, S. Wenzel, Y. Zhang

View

Tags: CUDA, HEP, High Energy Physics - Phenomenology, Intel Xeon Phi, nVidia, Physics, Tesla K20

December 3, 2016 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

View

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

November 23, 2016 by hgpu

Celeris: A GPU-accelerated open source software with a Boussinesq-type wave solver for real-time, interactive simulation and visualization

Sasan Tavakkol, Patrick Lynett

View

Tags: cfd, DirectX, Finite difference, Fluid dynamics, HLSL, nVidia, nVidia Quadro K600, Package, Physics, Rendering, Visualization

November 22, 2016 by hgpu

GPU-accelerated Red Blood Cells Simulations with Transport Dissipative Particle Dynamics

Ansel L. Blumers, Yu-Hang Tang, Zhen Li, Xuejin Li, George E. Karniadakis

View

Tags: Biological Physics, Computational Physics, CUDA, MPI, nVidia, Package, Physics, Tesla K20

November 22, 2016 by hgpu

Lattice QCD simulations using the OpenACC platform

Pushan Majumdar

View

Tags: CUDA, HEP, High Energy Physics - Lattice, nVidia, OpenACC, Physics, QCD, Tesla X2090

November 19, 2016 by hgpu

Data Acquisition with GPUs: The DAQ for the Muon g-2 Experiment at Fermilab

W. Gohn

View

Tags: CUDA, Data acquisition, nVidia, Physics, Tesla K40

November 16, 2016 by hgpu

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

Inside VOLT: Designing an Open-Source GPU Compiler (Tool)

SciDef: Automated Definition Extraction from Scientific Literature

SciDef: Automating Definition Extraction from Academic Literature with Large Language Models

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

BioAgent Bench: An AI Agent Evaluation Suite for Bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

See all packages

* * *

* * *

HGPU group © 2010-2026 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: