high performance computing on graphics processing units: hgpu.org

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OpenCL-Darknet: implementation and optimization of OpenCL-based deep learning object detection framework

Yongbon Koo, Sunghoon Kim, Young-guk Ha

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Tags: AMD Radeon R7, Computer science, CUDA, Deep learning, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 1050 Ti, OpenCL, Package

April 19, 2020 by hgpu

FlexTensor: An Automatic Schedule Exploration and Optimization Framework for Tensor Computation on Heterogeneous System

Size Zheng, Yun Liang, Shuo Wang, Renze Chen, Kaiwen Sheng

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Tags: Algorithms, Computer science, CUDA, FPGA, Heterogeneous systems, Machine learning, nVidia, OpenCL, Package, Tesla V100

April 19, 2020 by hgpu

A Study of Single and Multi-device Synchronization Methods in Nvidia GPUs

Lingqi Zhang, Mohamed Wahib, Haoyu Zhang, Satoshi Matsuoka

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia DGX-1, Package, Performance, Tesla P100, Tesla V100

April 19, 2020 by hgpu

MNN: A Universal and Efficient Inference Engine

Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, Lichuan Wang, Bin Zou, Yafeng Yang, Zongyang Cui, Yu Cai, Tianhang Yu, Chengfei Lv, Zhihua Wu

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Tags: Computer science, Deep learning, Neural networks, OpenCL, Package, Vulkan

April 12, 2020 by hgpu

Open Source Face Recognition API

Diogo Neto Coxinho Mourisco da Conceição

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Tags: Algorithms, Computer science, Computer vision, Databases, Face detection, Package, Python, Thesis

April 12, 2020 by hgpu

Neural Architecture Search for Lightweight Non-Local Networks

Yingwei Li, Xiaojie Jin, Jieru Mei, Xiaochen Lian, Linjie Yang, Cihang Xie, Qihang Yu, Yuyin Zhou, Song Bai, Alan Yuille

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Tags: Algorithms, Computer science, Deep learning, Neural networks, nVidia, Package, TensorFlow, Tesla V100

April 12, 2020 by hgpu

Deep Learning for Compilers

Chris Cummins

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Tags: AMD Radeon HD 7970, ATI, Benchmarking, Compilers, Computer science, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Package, Thesis

April 5, 2020 by hgpu

PyMatting: A Python Library for Alpha Matting

Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling

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Tags: Computer vision, CUDA, Image processing, nVidia, OpenCL, Package, PyOpenCL, Python

April 5, 2020 by hgpu

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Balazs Nagy, Philipp Foehn, Davide Scaramuzza

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Tags: Algorithms, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 960 M, nVidia Jetson TX2, OpenCV, Package

April 5, 2020 by hgpu

Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations

Sian Jin, Pascal Grosset, Christopher M. Biwer, Jesus Pulido, Jiannan Tian, Dingwen Tao, James Ahrens

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Tags: Astrophysics, Compression, Computational Physics, Cosmology, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2080 Ti, nVidia Quadro P 6000, Package, Physics, Tesla K80, Tesla P100, Tesla V100

April 5, 2020 by hgpu

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather

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Tags: Computer science, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 970, OpenCL, Package, Performance, Programming Languages

March 29, 2020 by hgpu

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning

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Tags: Computer science, NLP, nVidia, Package, Tesla V100, TPU

March 29, 2020 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

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

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

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

OpenCL-Darknet: implementation and optimization of OpenCL-based deep learning object detection framework

FlexTensor: An Automatic Schedule Exploration and Optimization Framework for Tensor Computation on Heterogeneous System

A Study of Single and Multi-device Synchronization Methods in Nvidia GPUs

MNN: A Universal and Efficient Inference Engine

Open Source Face Recognition API

Neural Architecture Search for Lightweight Non-Local Networks

Deep Learning for Compilers

PyMatting: A Python Library for Alpha Matting

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

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

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

Most viewed papers (last 30 days)