high performance computing on graphics processing units: hgpu.org

hgpu.org » Matrix inversion

Efficient Interleaved Batch Matrix Solvers for CUDA

Andrew Gloster, Enda Carroll, Miguel Bustamante, Lennon O'Naraigh

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Tags: Algorithms, Computational Physics, Computer science, CUDA, Matrix inversion, nVidia, Package, Tesla V100

September 15, 2019 by hgpu

A Data-oriented Method for Scheduling Dependent Tasks on High-density Multi-GPU Systems

Peng Zhang, Yuxiang Gao, Meikang Qiu

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Tags: Algorithms, Computer science, CUDA, Matrix inversion, Matrix multiplication, nVidia, Task scheduling, Tesla K40

August 3, 2015 by hgpu

GPU Declarative Framework

Robert W. Senser, Jr.

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Tags: AMD Radeon R9 270, ATI, Code generation, Computer science, Filtering, Image processing, Matrix inversion, nVidia, OpenCL, Sorting, Tesla S2050, Tesla T20, Thesis

December 1, 2014 by hgpu

Towards a Distributed GPU-Accelerated Matrix Inversion

Gerardo Ares, Pablo Ezzatti, Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Factorization, Matrix inversion, nVidia, Tesla C2050, Tesla M2050

August 18, 2013 by hgpu

Progressive High-Quality Response Surfaces for Visually Guided Sensitivity Analysis

I. Demir, R. Westermann

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Tags: Computer science, CUDA, Image generation, Matrix inversion, nVidia, nVidia GeForce GTX 580

July 29, 2013 by hgpu

Toward Accelerating the Matrix Inversion Computation of Symmetric Positive-Definite Matrices on Heterogeneous GPU-Based Systems

Huda Ibeid, Dinesh Kaushik, David Keyes, Hatem Ltaief

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Tags: Algorithms, Computer science, CUDA, Factorization, Heterogeneous systems, Matrix inversion, nVidia, Tesla C2070

December 14, 2011 by hgpu

A Short Note on Gaussian Process Modeling for Large Datasets using Graphics Processing Units

Mark Franey, Pritam Ranjan, Hugh Chipman

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Tags: Computer science, CUDA, Heterogeneous systems, Matrix inversion, nVidia, nVidia GeForce GTX 260, Statistics and Probability

November 26, 2011 by hgpu

Matrix inversion speed up with CUDA

Jorge Soriano Pinedo

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Tags: Algorithms, Computer science, CUDA, Image reconstruction, Matrix inversion, Matrix multiplication, nVidia, Tesla C1060, Thesis

October 28, 2011 by hgpu

Numerical Parallel Processing Based on GPU with CUDA Architecture

Chengming Zou, Chunfen Xia, Guanghui Zhao

Tags: Algorithms, Computer science, CUDA, Linear Algebra, Matrix inversion, nVidia

July 9, 2011 by hgpu

High Performance Matrix Inversion on a Multi-core Platform with Several GPUs

Pablo Ezzatti, Enrique S. Quintana-Orti, Alfredo Remon

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Tags: BLAS, Computer science, CUDA, Linear Algebra, Matrix inversion, nVidia, Presentation, Tesla C1060

July 5, 2011 by hgpu

Using graphics processors to accelerate the computation of the matrix inverse

P. Ezzatti, E. Quintana-Orti, A. Remon

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Tags: Computer science, Linear Algebra, Matrix inversion

April 21, 2011 by hgpu

Triangular matrix inversion on Graphics Processing Unit

Florian Ries, Tommaso De Marco, Matteo Zivieri, Roberto Guerrieri

Tags: Computer science, CUDA, Linear Algebra, Matrix inversion, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295

November 26, 2010 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

Efficient Interleaved Batch Matrix Solvers for CUDA

A Data-oriented Method for Scheduling Dependent Tasks on High-density Multi-GPU Systems

GPU Declarative Framework

Towards a Distributed GPU-Accelerated Matrix Inversion

Progressive High-Quality Response Surfaces for Visually Guided Sensitivity Analysis

Toward Accelerating the Matrix Inversion Computation of Symmetric Positive-Definite Matrices on Heterogeneous GPU-Based Systems

A Short Note on Gaussian Process Modeling for Large Datasets using Graphics Processing Units

Matrix inversion speed up with CUDA

Numerical Parallel Processing Based on GPU with CUDA Architecture

High Performance Matrix Inversion on a Multi-core Platform with Several GPUs

Using graphics processors to accelerate the computation of the matrix inverse

Triangular matrix inversion on Graphics Processing Unit

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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