high performance computing on graphics processing units: hgpu.org

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Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL

Ang Li, Hammad Mazhar, Radu Serban, Dan Negrut

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Tags: Computer science, CUDA, Linear Algebra, nVidia, OpenCL, Performance, Python, Sparse matrix, Tesla K20

February 22, 2015 by hgpu

Reproducible Triangular Solvers for High-Performance Computing

Roman Iakymchuk, David Defour, Sylvain Collange, Stef Graillat

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Tags: Algorithms, Computer science, Linear Algebra, nVidia, nVidia Quadro K5000, OpenCL, Package, Tesla K20

February 19, 2015 by hgpu

Nucleation Studies on Graphics Processing Units

Benjamin J. Block

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Tags: Anisotropy, ATI, ATI Radeon HD 6970, Biology, Chemistry, Differential equations, Ising model, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenCL, Physics, Tesla K20, Thesis

February 6, 2015 by hgpu

Comparison of OpenCL performance on different platforms using VexCL and Blaze

Hammad Mazhar, Dan Negrut

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Tags: AMD Radeon R7 260, ATI, Benchmarking, Computer science, Intel Xeon Phi, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 770, OpenCL, Performance, Sparse matrix, Tesla K20

February 6, 2015 by hgpu

GPGPU and MIC in Accelerated Cluster for Remote Sensed Image Processing Software

Olivier MELET, Toon Huysmans, Michel Hummel, Pierre-Marie Brunet

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Tags: CUDA, Image processing, Intel Xeon Phi, nVidia, nVidia GeForce GTX 480, OpenCL, Tesla K20

February 3, 2015 by hgpu

Multi-GPU Support on Shared Memory System using Directive-based Programming Model

Rengan Xu, Xiaonan Tian, Sunita Chandrasekaran, Barbara Chapman

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, Pthreads, Tesla K20

February 2, 2015 by hgpu

Characterizing and Enhancing Global Memory Data Coalescing on GPUs

Naznin Fauzia, Louis-Noel Pouchet, P. Sadayappan

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Tags: Computer science, CUDA, Memory, nVidia, Performance, PTX, Tesla K20

February 2, 2015 by hgpu

Locality-aware parallel block-sparse matrix-matrix multiplication using the Chunks and Tasks programming model

Emanuel H. Rubensson, Elias Rudberg

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, MPI, nVidia, Sparse matrix, Tesla K20

February 2, 2015 by hgpu

Performance Analysis and Optimization of a Distributed Processing Framework for Data Mining Accelerated with Graphics Processing Units

Edgar Isaac Hiroshi Leon Saiki

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Tags: Algorithms, Computer science, CUDA, Data mining, MapReduce, nVidia, nVidia GeForce GTX Titan, Tesla K20, Thesis

February 1, 2015 by hgpu

Accelerating Polynomial Homotopy Continuation on a Graphics Processing Unit with Double Double and Quad Double Arithmetic

Jan Verschelde, Xiangcheng Yu

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Tags: Algorithms, Computer science, CUDA, Extended precision, Linear Algebra, Mathematical Software, Numerical Analysis, nVidia, Package, Tesla K20

January 28, 2015 by hgpu

Reproducible and Accurate Matrix Multiplication for GPU Accelerators

Roman Iakymchuk, David Defour, Sylvain Collange, Stef Graillat

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Matrix multiplication, nVidia, OpenCL, Performance, Tesla K20

January 21, 2015 by hgpu

A Novel Implementation of QuickHull Algorithm on the GPU

Jiayin Zhang, Gang Mei, Nengxiong Xu, Kunyang Zhao

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Tags: Computational Geometry, Computer science, CUDA, Graphics, nVidia, Tesla K20

January 21, 2015 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL

Reproducible Triangular Solvers for High-Performance Computing

Nucleation Studies on Graphics Processing Units

Comparison of OpenCL performance on different platforms using VexCL and Blaze

GPGPU and MIC in Accelerated Cluster for Remote Sensed Image Processing Software

Multi-GPU Support on Shared Memory System using Directive-based Programming Model

Characterizing and Enhancing Global Memory Data Coalescing on GPUs

Locality-aware parallel block-sparse matrix-matrix multiplication using the Chunks and Tasks programming model

Performance Analysis and Optimization of a Distributed Processing Framework for Data Mining Accelerated with Graphics Processing Units

Accelerating Polynomial Homotopy Continuation on a Graphics Processing Unit with Double Double and Quad Double Arithmetic

Reproducible and Accurate Matrix Multiplication for GPU Accelerators

A Novel Implementation of QuickHull Algorithm on the GPU

Recent source codes

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

ROCm's implementation of Gromacs

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

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