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Using scheduling entropy amplification in CUDA/OpenMP code to exhibit non-reproducibility issues

David Defour

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Tags: Computer science, CUDA, Floating point error, nVidia, nVidia GeForce RTX 2060, OpenMP

November 6, 2022 by hgpu

Domain-Specific On-Device Object Detection Method

Seongju Kang, Jaegi Hwang, Kwangsue Chung

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Tags: Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce RTX 2060

January 9, 2022 by hgpu

EasyPBR: A Lightweight Physically-Based Renderer

Radu Alexandru Rosu, Sven Behnke

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Tags: 3D Graphics and Realism, Deep learning, nVidia, nVidia GeForce RTX 2060, OpenGL, Package, Python, Rendering, Visualization

December 13, 2020 by hgpu

Bit-level Parallelization of 3DES Encryption on GPU

Kaan Furkan Altınok, Afşin Peker, Alptekin Temizel

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1660, nVidia GeForce RTX 2060, Package, Security

July 26, 2020 by hgpu

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, Young-ri Choi

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Tags: Computer science, CUDA, Data parallelism, Deep learning, GPU cluster, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2060, nVidia Quadro P 4000, nVidia Titan RTX

May 31, 2020 by hgpu

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high performance computing on graphics processing units: hgpu.org

Using scheduling entropy amplification in CUDA/OpenMP code to exhibit non-reproducibility issues

Domain-Specific On-Device Object Detection Method

EasyPBR: A Lightweight Physically-Based Renderer

Bit-level Parallelization of 3DES Encryption on GPU

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

Recent source codes

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

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