high performance computing on graphics processing units: hgpu.org

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Towards a complete FEM-based simulation toolkit on GPUs: Geometric Multigrid solvers

Markus Geveler, Dirk Ribbrock, Dominik Goddeke, Peter Zajac, Stefan Turek

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Tags: Benchmarking, CUDA, Differential equations, FEM, Finite element method, Fluid dynamics, nVidia, nVidia GeForce GTX 285, Partial differential equations, PDEs, Sparse matrix

December 7, 2011 by hgpu

Parallel GMRES implementation for solving sparse linear systems on GPU clusters

Jacques M. Bahi, Raphael Couturier, Lilia Ziane Khodja

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Tags: Algorithms, Computer science, CUDA, GPU cluster, nVidia, Sparse matrix, Tesla C1060

November 25, 2011 by hgpu

A new approach for sparse matrix vector product on NVIDIA GPUs

F. Vazquez, J. J. Fernandez, E. M. Garzon

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Optimization, Sparse matrix

November 21, 2011 by hgpu

A Memory Efficient and Fast Sparse Matrix Vector Product on a GPU

A. Dziekonski, A. Lamecki, M. Mrozowski

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Tags: Computer science, CUDA, Electrodynamics, FEM, Finite element method, nVidia, nVidia GeForce GTX 480, Performance, Sparse matrix

October 26, 2011 by hgpu

Multifrontal computations on GPUs and their multi-core hosts

Robert F. Lucas, Gene Wagenbreth, Dan M. Davis, Roger Grimes

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Tags: Computer science, CUDA, Factorization, nVidia, Performance, Sparse matrix, Tesla C1060

October 7, 2011 by hgpu

Investigation on the Use of GPGPU for Fast Sparse Matrix Factorization

Ye Tian, Bin Zhou, Yutong Zhang, Ka Wing Chan

Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, Sparse matrix

October 7, 2011 by hgpu

A model-driven partitioning and auto-tuning integrated framework for sparse matrix-vector multiplication on GPUs

Ping Guo, He Huang, Qichang Chen, Liqiang Wang, En-Jui Lee, Po Chen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Performance, Software Engineering, Sparse matrix

September 19, 2011 by hgpu

A Fast GPU Implementation for Solving Sparse Ill-Posed Linear Equation Systems

Florian Stock, Andreas Koch

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Tags: Cell processor, Conjugate gradient solver, CUDA, FPGA, Image processing, Image reconstruction, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 280, Optimization, Sparse matrix

September 8, 2011 by hgpu

Parallel implementation of conjugate gradient method on graphics processors

Marcin Wozniak, Tomasz Olas, Roman Wyrzykowski

Tags: Algorithms, Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Sparse matrix, Tesla C1060

September 7, 2011 by hgpu

Towards a GPU-Based Simulation Framework for Deformable Surface Meshes

V. Kotamraju, S. Payandeh, J. Dill

Tags: 3D Graphics and Realism, Computer science, Dynamic surface, nVidia, nVidia GeForce 6600 GT, Sparse matrix

August 25, 2011 by hgpu

Improving the Performance of the Sparse Matrix Vector Product with GPUs

F. Vazquez, G. Ortega, J.J. Fernandez, E.M. Garzon

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Partial differential equations, PDEs, Sparse matrix

July 17, 2011 by hgpu

A Sparse Matrix Personality for the Convey HC-1

Krishna K. Nagar, Jason D. Bakos

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Tags: Computer science, CUDA, FPGA, nVidia, Performance, Sparse matrix, Tesla S1070

July 14, 2011 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Towards a complete FEM-based simulation toolkit on GPUs: Geometric Multigrid solvers

Parallel GMRES implementation for solving sparse linear systems on GPU clusters

A new approach for sparse matrix vector product on NVIDIA GPUs

A Memory Efficient and Fast Sparse Matrix Vector Product on a GPU

Multifrontal computations on GPUs and their multi-core hosts

Investigation on the Use of GPGPU for Fast Sparse Matrix Factorization

A model-driven partitioning and auto-tuning integrated framework for sparse matrix-vector multiplication on GPUs

A Fast GPU Implementation for Solving Sparse Ill-Posed Linear Equation Systems

Parallel implementation of conjugate gradient method on graphics processors

Towards a GPU-Based Simulation Framework for Deformable Surface Meshes

Improving the Performance of the Sparse Matrix Vector Product with GPUs

A Sparse Matrix Personality for the Convey HC-1

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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