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Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

Hanqiu Chen, Yahya Alhinai, Yihan Jiang, Eunjee Na, Cong Hao

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia RTX A6000, Package

October 16, 2022 by hgpu

Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

Wenzel Jakob, Sébastien Speierer, Nicolas Roussel, Delio Vicini

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Tags: 3D Graphics and Realism, Algorithms, Code generation, CUDA, nVidia, nVidia RTX A6000, Python, Rendering

February 6, 2022 by hgpu

GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration

Stefan Abi-Karam, Yuqi He, Rishov Sarkar, Lakshmi Sathidevi, Zihang Qiao, Cong Hao

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Tags: Computer science, FPGA, Machine learning, Neural networks, nVidia, nVidia RTX A6000, Package

January 30, 2022 by hgpu

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Martin Uray, Eduard Hirsch, Gerold Katzinger, Michael Gadermayr

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Tags: Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, nVidia RTX A6000

October 17, 2021 by hgpu

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

Three Contributions to the Theory and Practice of Optimizing Compilers

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

TorchOpt: An Efficient Library for Differentiable Optimization

NaturalCC: An Open-Source Toolkit for Code Intelligence

A Study on Neural-based Code Summarization in Low-resource Settings

mt4g: Memory Topology for GPUs

Capturing the Memory Topology of GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Recent source codes

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

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

SYCL-DNN: a library implementing neural network algorithms written using SYCL

SW#SYCL

Flink-TornadoVM-Artifact

StateMover: a checkpoint-based debugging framework for FPGAs

Kernel Tuner

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

NaturalCC: An Open-Source Toolkit for Code Intelligence

mt4g: Memory Topology for GPUs

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