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clSpMV: A Cross-Platform OpenCL SpMV Framework on GPUs

Bor-Yiing Su, Kurt Keutzer

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Tags: ATI, ATI Radeon HD 6970, Computer science, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Sparse matrix

May 30, 2012 by hgpu

A fight for performance and accuracy of the matrix multiplication routines: CUBLAS on Nvidia Tesla versus MKL and ATLAS on Intel Nehalem

Philippe Estival, Luc Giraud

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, Tesla T10

May 29, 2012 by hgpu

Java on CUDA architecture

Jan Strnad

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Tags: Computer science, CUDA, Image processing, Java, Linear Algebra, Matrix multiplication, nVidia, Thesis

May 24, 2012 by hgpu

Sparse direct solvers with accelerators over DAG runtimes

Xavier Lacoste, Pierre Ramet, Mathieu Faverge, Yamazaki Ichitaro, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse direct solvers, Tesla T20

May 24, 2012 by hgpu

C-DAC’s Efforts – Application Kernels on HPC Cluster with GPU Accelerators

VCV.Rao, Nisha Agrawa, Samrit Maity

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Tags: AMD FirePro V5900, AMD FirePro V7900, AMD FireStream 9350, ATI, Computer science, CUDA, Differential equations, FEM, Finite difference, Finite element method, GPU cluster, Heterogeneous systems, Linear Algebra, MPI, nVidia, OpenCL, Partial differential equations, PDEs, Tesla C2060

May 19, 2012 by hgpu

Random Walks based Multi-Image Segmentation: Quasiconvexity Results and GPU-based Solutions

Maxwell D. Collins, Jia Xu, Leo Grady, Vikas Singh

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Tags: Algorithms, CUDA, Image processing, Linear Algebra, nVidia, nVidia GeForce GTX 470, Optimization

April 28, 2012 by hgpu

An Automatic OpenCL Compute Kernel Generator for Basic Linear Algebra Operations

Philippe Tillet, Karl Rupp, Siegfried Selberherr

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Tags: Algorithms, Code generation, Computer science, Linear Algebra, nVidia, nVidia GeForce GTX 470, OpenCL, Package

April 21, 2012 by hgpu

Auto-tuning Dense Vector and Matrix-Vector Operations for Fermi GPUs

Hans Henrik Brandenborg Sorensen

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

April 18, 2012 by hgpu

High-Performance Matrix-Vector Multiplication on the GPU

Hans Henrik Brandenborg Sorensen

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

April 18, 2012 by hgpu

Auto-Tuning of Level 1 and Level 2 BLAS for GPUs

Hans Henrik Brandenborg Sorensen

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

April 16, 2012 by hgpu

Modular Arithmetic for Solving Linear Equations on the GPU

J. Hladik, I. Simecek

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Tags: Algorithms, Linear Algebra, Mathematics, Modular arithmetic, nVidia, OpenCL

April 11, 2012 by hgpu

New Basic Linear Algebra Methods for Simulation on GPUs

Jian Shi, Yiwei Zhang, Blake langland, Jijun Tang, Roger Dougal

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Tesla T10

April 9, 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

clSpMV: A Cross-Platform OpenCL SpMV Framework on GPUs

A fight for performance and accuracy of the matrix multiplication routines: CUBLAS on Nvidia Tesla versus MKL and ATLAS on Intel Nehalem

Java on CUDA architecture

Sparse direct solvers with accelerators over DAG runtimes

C-DAC’s Efforts – Application Kernels on HPC Cluster with GPU Accelerators

Random Walks based Multi-Image Segmentation: Quasiconvexity Results and GPU-based Solutions

An Automatic OpenCL Compute Kernel Generator for Basic Linear Algebra Operations

Auto-tuning Dense Vector and Matrix-Vector Operations for Fermi GPUs

High-Performance Matrix-Vector Multiplication on the GPU

Auto-Tuning of Level 1 and Level 2 BLAS for GPUs

Modular Arithmetic for Solving Linear Equations on the GPU

New Basic Linear Algebra Methods for Simulation on GPUs

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

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