high performance computing on graphics processing units: hgpu.org

hgpu.org » Differential equations

Initial condition for efficient mapping of level set algorithms on many-core architectures

Gabor Janos Tornai, Gyorgy Cserey

View

Download (PDF)

Source codes

Tags: Differential equations, nVidia, nVidia GeForce GTX 780, OpenCL, Package, Partial differential equations, PDEs, Signal processing

March 14, 2014 by hgpu

GPU Accelerated Discontinuous Galerkin Methods for Shallow Water Equations

R. Gandham, D. S. Medina, T. Warburton

View

Download (PDF)

Tags: Algorithms, ATI, ATI Radeon HD 7970, CUDA, Differential equations, Mathematics, Numerical Analysis, nVidia, OCCA, OpenCL, Partial differential equations, PDEs, Tesla C2050, Tesla K20

March 10, 2014 by hgpu

Optimizing Performance of Stencil Code with SPL Conqueror

Alexander Grebhahn, Norbert Siegmund, Sven Apel, Sebastian Kuckuk, Christian Schmitt, Harald Kostler

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Differential equations, Finite difference, Image processing, nVidia, OpenCL, Package, Partial differential equations, PDEs, Tesla K20

February 17, 2014 by hgpu

Parallel Algorithm for BSDEs Based High Dimensional American Option Pricing on the GPU

Ying Peng, Hui Liu, Shuzhen Yang, Bin Gong

View

Download (PDF)

Tags: Algorithms, CUDA, Differential equations, Finance, nVidia, Tesla C2050

January 10, 2014 by hgpu

Multilayered Abstractions for Partial Differential Equations

Graham Robert Markall

View

Download (PDF)

Tags: Algorithms, ATI, ATI Radeon HD 5870, Code generation, CUDA, Differential equations, FEM, Finite element method, Mathematics, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL, Partial differential equations, PDEs, Software Engineering, Thesis

December 11, 2013 by hgpu

Systematic Physics Constrained Parameter Estimation of Stochastic Differential Equations

Daniel Peavoy, Christian L. E. Franzke, Gareth O. Roberts

View

Download (PDF)

Tags: Algorithms, Bayesian, CUDA, Differential equations, nVidia, Physics

December 9, 2013 by hgpu

GPU-Accelerated Crack Path Computation Based on a Phase Field Approach for Brittle Fracture

Alexander Schluter, Adrian Willenbucher, Charlotte Kuhn

View

Download (PDF)

Tags: Computer science, CUDA, Differential equations, nVidia, Partial differential equations, PDEs, Tesla C2075

December 8, 2013 by hgpu

Uses of GPU Powered Interval Optimization for Parameter Identification in the Context of SO Fuel Cells

Stefan Kiel, Ekaterina Auer, Andreas Rauh

View

Download (PDF)

Tags: Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GTX 580, ODEs, Ordinary differential equations

October 15, 2013 by hgpu

CUDA-Accelerated ODETLAP: A Parallel Lossy Compression Implementation

Daniel N. Benedetti, W. Randolph Franklin, Wenli Li

View

Download (PDF)

Tags: Algorithms, Compression, CUDA, Differential equations, Image processing, nVidia, Partial differential equations, PDEs, Thrust

October 10, 2013 by hgpu

Multi-GPU Acceleration of Black-Scholes Equation based Option Pricing

Di Zhao, Jinhang Yu

View

Download (PDF)

Tags: Algorithms, CUDA, Differential equations, Finance, nVidia, nVidia GeForce GTX 660, Partial differential equations, PDEs

September 23, 2013 by hgpu

Exponential Integrators on Graphics Processing Units

Lukas Einkemmer, Alexander Ostermann

View

Download (PDF)

Tags: Computer science, CUDA, Differential equations, Numerical Analysis, nVidia, Tesla C1060, Tesla C2075

September 20, 2013 by hgpu

Simulation and modeling of physical broadcasts

Ahmed Ahmed

View

Download (PDF)

Tags: Cellular automata, Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GTX 680, Thesis

September 13, 2013 by hgpu

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

pyATF: Constraint-Based Auto-Tuning in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Checkpoint/Restore tool

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

KernelBench: Can LLMs Write GPU Kernels?

KernelBench: Can LLMs Write Efficient GPU Kernels?

An MLIR-based toolchain for AMD AI Engine-enabled devices

Seamless acceleration of Fortran intrinsics via AMD AI engines

Forecasting time series with constraints

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Initial condition for efficient mapping of level set algorithms on many-core architectures

GPU Accelerated Discontinuous Galerkin Methods for Shallow Water Equations

Optimizing Performance of Stencil Code with SPL Conqueror

Parallel Algorithm for BSDEs Based High Dimensional American Option Pricing on the GPU

Multilayered Abstractions for Partial Differential Equations

Systematic Physics Constrained Parameter Estimation of Stochastic Differential Equations

GPU-Accelerated Crack Path Computation Based on a Phase Field Approach for Brittle Fracture

Uses of GPU Powered Interval Optimization for Parameter Identification in the Context of SO Fuel Cells

CUDA-Accelerated ODETLAP: A Parallel Lossy Compression Implementation

Multi-GPU Acceleration of Black-Scholes Equation based Option Pricing

Exponential Integrators on Graphics Processing Units

Simulation and modeling of physical broadcasts

Recent source codes

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Checkpoint/Restore tool

KernelBench: Can LLMs Write GPU Kernels?

An MLIR-based toolchain for AMD AI Engine-enabled devices

Forecasting time series with constraints

Most viewed papers (last 30 days)