high performance computing on graphics processing units: hgpu.org

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Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

Jacob Austin, Rafael Corrales-Fatou, Sofia Wyetzner, Hod Lipson

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Package, Robotics

December 1, 2019 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

Hacking Neural Networks: A Short Introduction

Michael Kissner

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

November 24, 2019 by hgpu

A Computing Kernel for Network Binarization on PyTorch

Xianda Xu, Marco Pedersoli

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package

November 17, 2019 by hgpu

Deep Learning Based FPGA-CPU Acceleration

R. Luis Jalabert

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Tags: Code generation, Computer science, Deep learning, FPGA, Machine learning, Neural networks, Package, Thesis

November 17, 2019 by hgpu

Framework for Parallel Kernels Auto-tuning

Filip Petrovič

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Tags: Auto-Tuning, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce GTX 970, OpenCL, Package, Performance, Thesis

November 10, 2019 by hgpu

Study of OpenCL Processing Models for FPGA Devices

Piotr Szkotak, Pawel Russek, Kazimierz Wiatr

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Tags: Computer science, FPGA, OpenCL, Package, Security

November 10, 2019 by hgpu

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

Alexander Brandt, Davood Mohajerani, Marc Moreno Maza, Jeeva Paudel, Linxiao Wang

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Tags: Computer science, CUDA, LLVM, nVidia, Package, Performance

November 10, 2019 by hgpu

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

José Á. Morell, Andrés Camero, Enrique Alba

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Tags: Computer science, Deep learning, Distributed computing, Package, TensorFlow

November 3, 2019 by hgpu

Research on OpenCL optimization for FPGA deep learning application

Shuo Zhang, Yanxia Wu, Chaoguang Men, Hongtao He, Kai Liang

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Tags: Computer science, Deep learning, FPGA, OpenCL, Package

November 3, 2019 by hgpu

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Tomasz Kornuta

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Tags: Computer science, Computer vision, Machine learning, Neural networks, Package, Python

October 27, 2019 by hgpu

Performance Evaluation of Advanced Features in CUDA Unified Memory

Steven W. D. Chien, Ivy B. Peng, Stefano Markidis

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 1050, Package, Prefetch, Tesla V100

October 27, 2019 by hgpu

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

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high performance computing on graphics processing units: hgpu.org

Applications

Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

Benchmarking Deep Learning Models on Jetson TX2

Hacking Neural Networks: A Short Introduction

A Computing Kernel for Network Binarization on PyTorch

Deep Learning Based FPGA-CPU Acceleration

Framework for Parallel Kernels Auto-tuning

Study of OpenCL Processing Models for FPGA Devices

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

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

Research on OpenCL optimization for FPGA deep learning application

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Performance Evaluation of Advanced Features in CUDA Unified Memory

Recent source codes

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

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