high performance computing on graphics processing units: hgpu.org

hgpu.org » Linear Algebra

A Scalable Framework for Heterogeneous GPU-Based Clusters

Fengguang Song, Jack Dongarra, Stanimire Tomov

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Tags: Algorithms, Computer science, CUDA, Factorization, GPU cluster, Heterogeneous systems, Linear Algebra, MPI, nVidia, Tesla M2070

April 7, 2012 by hgpu

Novel GPU Implementation of Jacobi Algorithm for Karhunen-Loeve Transform of Dense Matrices

Mustafa U. Torun, Onur Yilmaz, Ali N. Akansu

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Tesla C2070

April 4, 2012 by hgpu

CUDA 2D Stencil Computations for the Jacobi Method

Jose Maria Cecilia, Jose Manuel Garcia, Manuel Ujaldon

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Differential equations, Linear Algebra, nVidia, Optimization, Tesla C1060, Tesla C870

March 16, 2012 by hgpu

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Dimitar Lukarski

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Tags: Algorithms, Computer science, CUDA, Differential equations, Factorization, FEM, Finite difference, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 580, Partial differential equations, PDEs, Sparse matrix, Tesla M1060, Tesla S1070, Thesis

March 16, 2012 by hgpu

Multi-platform Linear Algebra

E. Ketelaer, D. Lukarski

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Tags: Computer science, CUDA, Differential equations, Distributed data structures, Linear Algebra, nVidia, Package, Partial differential equations, PDEs

March 16, 2012 by hgpu

Advancing Large Scale Many-Body QMC Simulations on GPU Accelerated Multicore Systems

Andres Tomas, Chia-Chen Chang, Richard Scalettar, Zhaojun Bai

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Tags: Computer science, CUDA, Linear Algebra, Monte Carlo simulation, nVidia, QMC, Tesla C2050

February 15, 2012 by hgpu

Automatic Performance Optimization in ViennaCL for GPUs

Karl Rupp, Josef Weinbub, Florian Rudolf

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Tags: Algorithms, ATI, ATI Radeon HD 5850, Computer science, Linear Algebra, nVidia, nVidia GeForce 8500 GT, nVidia GeForce GTX 470, OpenCL, Optimization, Package, Performance, Programming techniques

February 10, 2012 by hgpu

Parallel Semi-Implicit Time Integrators

B. Ong, A. Melfi, A. Christlieb

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Partial differential equations, PDEs, Tesla C1060

February 9, 2012 by hgpu

CUDA Expression Templates

Paul Wiemann, Stephan Wenger, Marcus Magnor

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTS 250, Package

January 30, 2012 by hgpu

Dense Linear Algebra on Distributed Heterogeneous Hardware with a Symbolic DAG Approach

George Bosilca, Aurelien Bouteiller, Anthony Danalis, Thomas Herault, Piotr Luszczek, Jack J. Dongarra

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Tags: Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Tesla C2070

January 30, 2012 by hgpu

Determinant Computation on the GPU using the Condensation Method

Sardar Anisul Haque, Marc Moreno Maza

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Tesla C2050

January 23, 2012 by hgpu

Asymptotic Peak Utilisation in Heterogeneous Parallel CPU/GPU Pipelines: A Decentralised Queue Monitoring Strategy

Michael T. Garba, Horacio Gonzalez-Velez

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Tags: Computer science, Heterogeneous systems, Linear Algebra, Programming techniques

January 19, 2012 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

A Scalable Framework for Heterogeneous GPU-Based Clusters

Novel GPU Implementation of Jacobi Algorithm for Karhunen-Loeve Transform of Dense Matrices

CUDA 2D Stencil Computations for the Jacobi Method

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Multi-platform Linear Algebra

Advancing Large Scale Many-Body QMC Simulations on GPU Accelerated Multicore Systems

Automatic Performance Optimization in ViennaCL for GPUs

Parallel Semi-Implicit Time Integrators

CUDA Expression Templates

Dense Linear Algebra on Distributed Heterogeneous Hardware with a Symbolic DAG Approach

Determinant Computation on the GPU using the Condensation Method

Asymptotic Peak Utilisation in Heterogeneous Parallel CPU/GPU Pipelines: A Decentralised Queue Monitoring Strategy

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

Most viewed papers (last 30 days)