high performance computing on graphics processing units: hgpu.org

hgpu.org » PDEs

Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

S. Cuomo, A. Galletti, G. Giunta, L. Marcellino

View

Tags: Algorithms, Computer science, CUDA, Differential equations, GPU cluster, Heterogeneous systems, nVidia, nVidia Quadro K5000, Optical flow, Partial differential equations, PDEs

June 24, 2015 by hgpu

A Parallel Implementation of the Galerkin Method for Solving Partial Differential Equations on a Triangular Mesh

Rachel N. Hess

View

Tags: Differential equations, FEM, Finite element method, Mathematics, OpenCL, Partial differential equations, PDEs, PyOpenCL, Thesis, Triangular meshes

June 7, 2015 by hgpu

High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study

Gloria Ortega Lopez

View

Tags: Computer science, CUDA, Differential equations, FEM, Finite difference, Finite element method, GPU cluster, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Package, Partial differential equations, PDEs, Sparse matrix, Tesla C2050, Tesla M2070, Tesla M2090, Thesis, Tomography

March 30, 2015 by hgpu

A Switched Dynamical System Framework for Analysis of Massively Parallel Asynchronous Numerical Algorithms

Kooktae Lee, Raktim Bhattacharya, Vijay Gupta

View

Tags: Algorithms, Computer science, CUDA, Differential equations, nVidia, Partial differential equations, PDEs, Tesla C2050

March 18, 2015 by hgpu

Performance Analysis and Optimization of Hermite Methods on NVIDIA GPUs Using CUDA

Evan T. Dye

View

Tags: Algorithms, Computer science, CUDA, Differential equations, Mathematics, nVidia, nVidia GeForce GTX 560 Ti, Partial differential equations, PDEs, Thesis

February 2, 2015 by hgpu

Productive and Efficient Computational Science Through Domain-specific Abstractions

Florian Rathgeber

View

Tags: Computer science, CUDA, Differential equations, FEM, Finite element method, nVidia, OpenCL, Partial differential equations, PDEs, Thesis

February 1, 2015 by hgpu

Global finite element matrix construction based on a CPU-GPU implementation

Francisco Javier Ramírez-Gil, Marcos de Sales Guerra Tsuzuki, Wilfredo Montealegre-Rubio

View

Tags: Analysis of PDEs, Computational Engineering, CUDA, FEM, Numerical Analysis, parallel computing, PDEs, Performance

January 21, 2015 by fjramireg

A CUDA implementation of the High Performance Conjugate Gradient benchmark

Everett Phillips, Massimiliano Fatica

View

Tags: Algorithms, Benchmarking, Computer science, CUDA, Differential equations, nVidia, Partial differential equations, PDEs, Tesla K20

November 29, 2014 by hgpu

A stencil-based implementation of Parareal in the C++ domain specific embedded language STELLA

Andrea Arteaga, Daniel Ruprecht, Rolf Krause

View

Tags: Computer science, CUDA, Differential equations, MPI, nVidia, Partial differential equations, PDEs, Tesla K20

October 3, 2014 by hgpu

A CUDA Back-End for the Equelle Compiler

Havard Heitlo Holm

View

Tags: Compilers, Computer science, CUDA, Differential equations, Finite volume method, Heterogeneous systems, nVidia, nVidia Quadro NVS 5200 M, Package, Partial differential equations, PDEs, Tesla K40, Thesis

September 8, 2014 by hgpu

Algorithms for Solving Non-Stationary Heat Conduction Problem for Design of a Technical Device

Alexander Ayriyan, Jan Busa Jr., Eugeny E. Donets, Hovik Grigorian, Jan Pribis

View

Tags: Algorithms, Computational Physics, Differential equations, Finite difference, nVidia, nVidia GeForce GTX 470, OpenCL, Partial differential equations, PDEs, Physics

August 27, 2014 by hgpu

Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs

Moritz Schmid, Oliver Reiche, Christian Schmitt, Frank Hannig, Jurgen Teich

View

Tags: ARM, Code generation, Compilers, Computational Complexity, Computer science, Differential equations, FPGA, Image processing, Partial differential equations, PDEs

August 23, 2014 by hgpu

stdgpu: Efficient STL-like Data Structures on the GPU

stdgpu: Efficient STL-like Data Structures on the GPU

ikra-cpp: C++ Library for Object-oriented Programming with Structure of Arrays Layout

Memory-Efficient Object-Oriented Programming on GPUs

Mass_Flow_Estimation: Semi-supervised implementation of a DNN for mass prediction from sparsely labeled images

Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth

KNN: k-Nearest Neighbours algorithm

High Performance Computing via High Level Synthesis

SODECL: library of ordinary differential equation (ODE) and stochastic differential equation (SDE) solvers in OpenCL

SODECL: An Open Source Library for Calculating Multiple Orbits of a System of Stochastic Differential Equations in Parallel

sisph: Simple Iterative Incompressible Smoothed Particle Hydrodynamics

Simple Iterative Incompressible Smoothed Particle Hydrodynamics

GraphBLAST: High-Performance Linear Algebra-based Graph Primitives on GPUs

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

FAST: Framework for Heterogeneous Medical Image Computing and Visualization

Parallelization of Coherent Point Drift for patient registration

ESBMC-GPU

Incremental Bounded Model Checking of Artificial Neural Networks in CUDA

FBLAS: BLAS implementation for Intel FPGA

FBLAS: Streaming Linear Algebra on FPGA

See all packages

* * *

* * *

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

+

HGPU group © 2010-2019 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: