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Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

Robert Dietrich

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Tags: Computer science, CUDA, Data acquisition, Heterogeneous systems, MPI, nVidia, OpenACC, OpenCL, Tesla K80, Tesla V100, Thesis

December 1, 2019 by hgpu

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Xiaying Wang, Michele Magno, Lukas Cavigelli, Luca Benini

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Tags: Algorithms, Computer science, IoT, Machine learning, Neural networks, Package

December 1, 2019 by hgpu

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

FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads

Guoping Long, Jun Yang, Wei Lin

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Tags: Computer science, CUDA, Deep learning, nVidia, Performance, Tesla V100

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

Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Keshav Pingali

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Tags: Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 1080 Ti, Tesla V100

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

FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10

Ke He, Bo Liu, Yu Zhang, Andrew Ling, Dian Gu

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Tags: Caffe, Computer science, Deep learning, FPGA, Neural networks, OpenCL

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

Compiler-Driven Performance on Heterogeneous Computing Platforms

Artem Chikin

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, Hybrid computing, nVidia, Performance, Tesla P100, Tesla V100, Thesis

November 17, 2019 by hgpu

word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

Aliakbar Panahi, Seyran Saeedi, Tom Arodz

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Tesla V100

November 17, 2019 by hgpu

A Highly Parameterizable Framework for Conditional Restricted Boltzmann Machine Based Workloads Accelerated With FPGAs and OpenCL

Zoran Jaksic, Nicola Cadenelli, David Buchaca Prats, Jorda Polo, Josep Lluis Berral Garcia, David Carrera Perez

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Tags: Computer science, FPGA, HLS, Linear Algebra, Matrix multiplication, Neural networks, OpenCL

November 17, 2019 by hgpu

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

PhysProver: Advancing Automatic Theorem Proving for Physics

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

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

FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads

Benchmarking Deep Learning Models on Jetson TX2

Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

Hacking Neural Networks: A Short Introduction

FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10

A Computing Kernel for Network Binarization on PyTorch

Compiler-Driven Performance on Heterogeneous Computing Platforms

word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

A Highly Parameterizable Framework for Conditional Restricted Boltzmann Machine Based Workloads Accelerated With FPGAs and OpenCL

Recent source codes

Awesome LLM-Driven Kernel Generation

PhysProver: Advancing Automatic Theorem Proving for Physics

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

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