high performance computing on graphics processing units: hgpu.org

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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

Interoperable GPU Kernels as Latency Improver for MEC

Juuso Haavisto, Jukka Riekki

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Tags: Computer science, GLSL, Graphics, nVidia, nVidia GeForce RTX 2080, nVidia Jetson TX2, Package, Vulkan

February 2, 2020 by hgpu

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Lukas Sommer, Florian Stock, Leonardo Solis-Vasquez, Andreas Koch

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Tags: Automotive, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, OpenCL, OpenMP

January 26, 2020 by hgpu

Benchmarking Deep Learning Models on Jetson TX2

Lucas Pedro Bordignon, Aldo von Wangenheim

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, Image recognition, nVidia, nVidia Jetson TX2, Overview, Package, Tutorial

November 24, 2019 by hgpu

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Valentin Radu, Kuba Kaszyk, Yuan Wen, Jack Turner, Jose Cano, Elliot J. Crowley, Bjorn Franke, Amos Storkey, Michael O'Boyle

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia Jetson TX2, OpenCL

October 13, 2019 by hgpu

Survey and Benchmarking of Machine Learning Accelerators

Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner

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Tags: ASIC, Benchmarking, Computer science, CUDA, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, nVidia Jetson TX2, Performance, TPU

September 1, 2019 by hgpu

A Power Efficient Neural Network Implementation on Heterogeneous FPGA and GPU Devices

Yuexuan Tu, Saad Sadiq, Yudong Tao, Mei-Ling Shyu, Shu-Ching Chen

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Tags: Computer science, Deep learning, Energy-efficient computing, FPGA, Neural networks, nVidia, nVidia Jetson TX2

July 28, 2019 by hgpu

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

Murad Qasaimeh, Kristof Denolf, Jack Lo, Kees Vissers, Joseph Zambreno, Phillip H. Jones

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Tags: ARM, Benchmarking, Computer science, Computer vision, CUDA, FPGA, nVidia, nVidia Jetson TX2, OpenCV, Package

May 26, 2019 by hgpu

Predictable GPGPU Computing in DNN-Driven Autonomous Systems

Husheng Zhou

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 620, nVidia GeForce GTX 660, nVidia Jetson TX2, nVidia Quadro 6000, Tesla K80, Thesis

May 12, 2019 by hgpu

Real world applications of Artificial Intelligence on constrained hardware

Wiebe Van Ranst

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Tags: Artificial intelligence, Computer science, Computer vision, Image processing, nVidia, nVidia Jetson TX2, OpenCL, Thesis

April 20, 2019 by hgpu

A Survey on Optimized Implementation of Deep Learning Models on the NVIDIA Jetson Platform

Sparsh Mittal

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia Jetson TK1, nVidia Jetson TX1, nVidia Jetson TX2, OpenCL, survey

December 30, 2018 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

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

Interoperable GPU Kernels as Latency Improver for MEC

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Benchmarking Deep Learning Models on Jetson TX2

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Survey and Benchmarking of Machine Learning Accelerators

A Power Efficient Neural Network Implementation on Heterogeneous FPGA and GPU Devices

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

Predictable GPGPU Computing in DNN-Driven Autonomous Systems

Real world applications of Artificial Intelligence on constrained hardware

A Survey on Optimized Implementation of Deep Learning Models on the NVIDIA Jetson Platform

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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