high performance computing on graphics processing units: hgpu.org

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A Stream Processor Cluster Architecture Model with the Hybrid Technology of MPI and CUDA

Qing-kui Chen, Jia-kang Zhang

Tags: Computer science, CUDA, GPU cluster, MPI, nVidia, Programming techniques

March 4, 2011 by hgpu

Formal Description and Optimization Based High – Performance Computing on CUDA

Bo Li, Huacheng Zhao, Jingjing Liang

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8600 GT, Optimization, Programming techniques

March 4, 2011 by hgpu

Scene Recognition Acceleration Using CUDA and OpenMP

Yuxin Wang, Zhen Feng, He Guo, Changqin He, Yuansheng Yang

Tags: Computer science, Computer vision, CUDA, Image recognition, nVidia

March 4, 2011 by hgpu

Towards a Software Transactional Memory for Graphics Processors

Daniel Cederman, Philippas Tsigas and Muhammad Tayyab Chaudhry

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, Programming techniques

March 4, 2011 by hgpu

Architecture-Aware Optimization Targeting Multithreaded Stream Computing

Byunghyun Jang, Synho Do, Homer Pien, David Kaeli

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Tags: ATI, ATI Radeon HD 3870, Brook, Computer science, Optimization, Programming techniques

March 4, 2011 by hgpu

Real-Time Multiprocessor Systems with GPUs

Glenn A. Elliott and James H. Anderson

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Performance, Task scheduling

March 3, 2011 by hgpu

Smooth Mixed-Resolution GPU Volume Rendering

Johanna Beyer, Markus Hadwiger, Torsten Moller, Laura Fritz

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 280, OpenGL, Rendering, Visualization

March 3, 2011 by hgpu

The sparse matrix vector product on GPUs

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

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

March 3, 2011 by hgpu

Unified – A Sharp Turn in the Latest Era of Graphic Processors

Asma Kausar, Aziz Khan, Zulfiqar Ali

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Programming techniques, Review

March 3, 2011 by hgpu

RankBoost Acceleration on both NVIDIA CUDA and ATI Stream Platforms

Bo Wang, Tianji Wu, Feng Yan, Ruirui Li, Ningyi Xu, Yu Wang

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Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 4870, ATI Stream, Computer science, CUDA, nVidia, nVidia GeForce 9800 GTX

March 3, 2011 by hgpu

Parallel Cycle Based Logic Simulation Using Graphics Processing Units

Alper Sen, Baris Aksanli, Murat Bozkurt, Melih Mert

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Tags: Computer science, CUDA, Electronic design automation, nVidia, nVidia Quadro FX 3800

March 3, 2011 by hgpu

Speeding Up Cycle Based Logic Simulation Using Graphics Processing Units

Alper Sen, Baris Aksanli, Murat Bozkurt

Tags: Algorithms, Computer science, CUDA, Electronic design automation, Graph theory, nVidia

March 3, 2011 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

A Stream Processor Cluster Architecture Model with the Hybrid Technology of MPI and CUDA

Formal Description and Optimization Based High – Performance Computing on CUDA

Scene Recognition Acceleration Using CUDA and OpenMP

Towards a Software Transactional Memory for Graphics Processors

Architecture-Aware Optimization Targeting Multithreaded Stream Computing

Real-Time Multiprocessor Systems with GPUs

Smooth Mixed-Resolution GPU Volume Rendering

The sparse matrix vector product on GPUs

Unified – A Sharp Turn in the Latest Era of Graphic Processors

RankBoost Acceleration on both NVIDIA CUDA and ATI Stream Platforms

Parallel Cycle Based Logic Simulation Using Graphics Processing Units

Speeding Up Cycle Based Logic Simulation Using Graphics Processing Units

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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