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A File System Using GPU-Accelerated File-wise Reliability Scheme

Chien-Kai Tseng, Shang-Chieh Lin, Yarsun Hsu

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Tags: Computer science, CUDA, Fault tolerance, nVidia, Tesla M2050

August 30, 2012 by hgpu

Fault Tree Analysis Speed-up with GPU Parallel Computing

Hadi Aghassi, Farrokh Aghassi

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Tags: Algorithms, Computer science, CUDA, Fault tolerance, Monte Carlo simulation, nVidia, nVidia GeForce 8600 GS

July 1, 2012 by hgpu

PartialRC: A Partial Recomputing Method for Efficient Fault Recovery on GPGPUs

Xin-Hai Xu, Xue-Jun Yang, Jing-Ling Xue, Yu-Fei Lin, Yi-Song Lin

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Tags: Computer science, CUDA, Fault tolerance, Heterogeneous systems, nVidia, nVidia GeForce GTX 295

March 6, 2012 by hgpu

G-CP: Providing Fault Tolerance on the GPU through Software Checkpointing

Felix Loh, Matt Sinclair

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Tags: Algorithms, Computer science, CUDA, Fault tolerance, nVidia, Tesla C1060

December 25, 2011 by hgpu

Soft Error Resilient QR Factorization for Hybrid System

Peng Du, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Fault tolerance, Hybrid computing, Linear Algebra, nVidia, Tesla C2050, Tesla M2070

November 20, 2011 by hgpu

Natural HPC substrate: Exploitation of mixed multicore CPU and GPUs

Moussa Taifi, Abdallah Khreishah, Justin Y. Shi

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Tags: Computer science, CUDA, Fault tolerance, Heterogeneous systems, nVidia, nVidia GeForce 8400 GS, Tesla C1060

November 15, 2011 by hgpu

Sustainable GPU Computing at Scale

Justin Y. Shi, Moussa Taifi, Abdallah Khreishah, Jie Wu

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Tags: Computer science, CUBLAS, CUDA, Energy-efficient computing, Fault tolerance, nVidia, OpenMPI, Tesla S1070

November 12, 2011 by hgpu

Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

Keun Soo Yim, Cuong Pham, Mushfiq Saleheen, Zbigniew Kalbarczyk, Ravishankar Iyer

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Tags: Computer science, CUDA, Error recovery, Fault tolerance, nVidia, Tesla S1070

September 25, 2011 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

A File System Using GPU-Accelerated File-wise Reliability Scheme

Fault Tree Analysis Speed-up with GPU Parallel Computing

PartialRC: A Partial Recomputing Method for Efficient Fault Recovery on GPGPUs

G-CP: Providing Fault Tolerance on the GPU through Software Checkpointing

Soft Error Resilient QR Factorization for Hybrid System

Natural HPC substrate: Exploitation of mixed multicore CPU and GPUs

Sustainable GPU Computing at Scale

Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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