high performance computing on graphics processing units: hgpu.org

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An approach for the effective utilization of GP-GPUs in parallel combined simulation

David W. Bauer, Matthew McMahon, Ernest H. Page

Tags: Computer science, Heterogeneous systems, Partial differential equations, PDEs, Performance

March 21, 2011 by hgpu

The GPU as a high performance computational resource

Tor Dokken, Trond R Hagen, Jon M Hjelmervik

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Tags: Computer science, Linear Algebra, Mathematical Software, Partial differential equations, PDEs, Review

March 11, 2011 by hgpu

Application of Assembly of Finite Element Methods on Graphics Processors for Real-Time Elastodynamics

Cris Cecka, Adrian Lew, Eric Darve

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Tags: CUDA, Finite element method, Mathematics, nVidia, nVidia GeForce GTX 480, Partial differential equations, PDEs

March 2, 2011 by hgpu

Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations (Part 2: Double Precision GPUs)

Dominik Goddeke and Robert Strzodka

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Tags: Computer science, Conjugate gradient solver, CUDA, Extended precision, Mixed precision, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Partial differential equations, PDEs, Sparse linear iterative solvers

March 2, 2011 by hgpu

Accelerating Double Precision FEM Simulations with GPUs

Dominik Goddeke, Robert Strzodka, Stefan Turek

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Tags: Computer science, Extended precision, Finite element method, nVidia, nVidia GeForce 6800, OpenGL, Partial differential equations, PDEs

March 2, 2011 by hgpu

Integrating GPUs as fast co-processors into the existing parallel FE package FEAST

Dominik Goddeke, Christian Becker, Stefan Turek

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Tags: Finite element method, Fluid dynamics, nVidia, nVidia Quadro FX 1400, Package, Partial differential equations, PDEs

March 2, 2011 by hgpu

Mixed-Precision GPU-Multigrid Solvers with Strong Smoothers

Dominik Goddeke, Robert Strzodka

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Tags: CUDA, Mathematics, Mixed precision, nVidia, nVidia GeForce GTX 280, Partial differential equations, PDEs

March 2, 2011 by hgpu

GPU Acceleration of Equations Assembly in Finite Elements Method – Preliminary Results

Jiri Filipovic, Igor Peterlik, Jan Fousek

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Tags: Computer science, CUDA, Finite element method, nVidia, nVidia GeForce GTX 280, Partial differential equations, PDEs

February 21, 2011 by hgpu

Numerical Simulation of the Complex Ginzburg-Landau Equation on GPUs with CUDA

K.A. Hawick and D.P. Playne

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Tags: CUDA, Finite difference, nVidia, nVidia GeForce GTX 480, Partial differential equations, PDEs, Physics, Thermodynamics, Visualization

February 14, 2011 by hgpu

Modelling, simulating and visualising the Cahn-Hilliard-Cook field equation

K.A. Hawick and D.P.Playne

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Tags: Algorithms, Alloys, Finite difference, Partial differential equations, PDEs, Physics, Thermodynamics, Visualization

February 14, 2011 by hgpu

Automatically Generating Efficient Simulation Codes on GPUs from Partial Differential Equations

K.A. Hawick and D.P. Playne

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Tags: Code generation, Compilers, Computer science, CUDA, Finite difference, nVidia, Partial differential equations, PDEs

February 14, 2011 by hgpu

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

K.A. Hawick, A. Leist, and D.P. Playne

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Tags: Cell processor, Computer science, CUDA, Data parallelism, Finite difference, nVidia, nVidia GeForce GTX 295, OpenCL, Partial differential equations, PDEs, Playstation, Pseudo-random number generators

February 14, 2011 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

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

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

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

Generating Literature-Driven Scientific Theories at Scale

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high performance computing on graphics processing units: hgpu.org

An approach for the effective utilization of GP-GPUs in parallel combined simulation

The GPU as a high performance computational resource

Application of Assembly of Finite Element Methods on Graphics Processors for Real-Time Elastodynamics

Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations (Part 2: Double Precision GPUs)

Accelerating Double Precision FEM Simulations with GPUs

Integrating GPUs as fast co-processors into the existing parallel FE package FEAST

Mixed-Precision GPU-Multigrid Solvers with Strong Smoothers

GPU Acceleration of Equations Assembly in Finite Elements Method – Preliminary Results

Numerical Simulation of the Complex Ginzburg-Landau Equation on GPUs with CUDA

Modelling, simulating and visualising the Cahn-Hilliard-Cook field equation

Automatically Generating Efficient Simulation Codes on GPUs from Partial Differential Equations

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

Recent source codes

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

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

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

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

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

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