high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

QP: A Heterogeneous Multi-Accelerator Cluster

Michael Showerman, Jeremy Enos, Avneesh Pant, Volodymyr Kindratenko, Craig Steffen, Robert Pennington, Wen-mei Hwu

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Tags: Computer science, CUDA, FPGA, GPU cluster, nVidia, nVidia Quadro FX 5600, Review

February 4, 2011 by hgpu

On testing GPU memory for hard and soft errors

Guochun Shi, Jeremy Enos, Michael Showerman, Volodymyr Kindratenko

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Source codes

Tags: Computer science, CUDA, Hardware Architecture, Memory, nVidia, Package, Tesla C1060, Tesla S1070

February 3, 2011 by hgpu

Quantifying the Impact of GPUs on Performance and Energy Efficiency in HPC Clusters

J. Enos, C. Steffen, J. Fullop, M. Showerman, G. Shi, K. Esler, V. Kindratenko, J. Stone, J. Phillips

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Tags: Computer science, CUDA, Energy-efficient computing, GPU cluster, nVidia, Performance

February 3, 2011 by hgpu

MILC on GPUs

Gushing Shi, Steven Gottlieb, Volodymyr Kindratenko

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Tags: Conjugate gradient solver, CUDA, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce GTX 280, Physics, QCD

February 3, 2011 by hgpu

3I: A tool for visualizing and processing in parallel 2D & 3D images

Juan P.D'Amato, Maria V.Cifuentes, Pablo Lotito

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Tags: 3D Graphics and Realism, CUDA, Image processing, nVidia, nVidia GeForce GTX 285

February 3, 2011 by hgpu

3D Registration Based on Normalized Mutual Information: Performance of CPU vs. GPU Implementation

Florian Jung, Stefan Wesarg

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Tags: CUDA, Image processing, Image registration, Medicine, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 285

February 3, 2011 by hgpu

3D Information Extraction Based on GPU

Ying Zhang

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce 8400 M GS, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, nVidia Quadro FX 3700, Tesla C1060

February 3, 2011 by hgpu

3D GPU Architecture using Cache Stacking: Performance, Cost, Power and Thermal analysis

Ahmed Al Maashri, Guangyu Sun, Xiangyu Dong, Vijay Narayanan and Yuan Xie

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Tags: Computer science, CUDA, Hardware Architecture, Memory, Memory model, nVidia, nVidia GeForce 6400 GT

February 3, 2011 by hgpu

3D finite element numerical integration on GPUs

Pawel Maciol, Przemyslaw Plaszewski and Krzysztof Banasa

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Tags: Computer science, CUDA, Finite element method, nVidia, nVidia GeForce 8800 GTX

February 3, 2011 by hgpu

Data access optimized applications on the GPU using NVIDIA CUDA

Dheevatsa Mudiger

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Tags: Computer science, CUDA, Data Structures and Algorithms, Fluid dynamics, nVidia, Tesla C1060, Tesla C870, Thesis

February 3, 2011 by hgpu

High Performance Power Spectrum Analysis Using a FPGA Based Reconfigurable Computing Platform

Yogindra Abhyankar, Sajish C., Yogesh Agarwal, C.R. Subrahmanya, Peeyush Prasad

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Tags: FFT, FPGA, Signal processing

February 3, 2011 by hgpu

Real-time PCA calculation for spectral imaging (using SIMD and GP-GPU)

Radovan Josth, Jukka Antikainen, Jiri Havel, Adam Herout, Pavel Zemcik, Markku Hauta-Kasari

Tags: CUDA, Image processing, nVidia

February 2, 2011 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

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high performance computing on graphics processing units: hgpu.org

Applications

QP: A Heterogeneous Multi-Accelerator Cluster

On testing GPU memory for hard and soft errors

Quantifying the Impact of GPUs on Performance and Energy Efficiency in HPC Clusters

MILC on GPUs

3I: A tool for visualizing and processing in parallel 2D & 3D images

3D Registration Based on Normalized Mutual Information: Performance of CPU vs. GPU Implementation

3D Information Extraction Based on GPU

3D GPU Architecture using Cache Stacking: Performance, Cost, Power and Thermal analysis

3D finite element numerical integration on GPUs

Data access optimized applications on the GPU using NVIDIA CUDA

High Performance Power Spectrum Analysis Using a FPGA Based Reconfigurable Computing Platform

Real-time PCA calculation for spectral imaging (using SIMD and GP-GPU)

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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