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Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks

Philipp Gysel

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla K40, Thesis

May 23, 2016 by hgpu

ImageCL: An Image Processing Language for Performance Portability on Heterogeneous Systems

Thomas L. Falch, Anne C. Elster

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Tags: ATI, ATI Radeon HD 7970, Computer science, Image processing, nVidia, nVidia GeForce GTX 960, OpenCL, Performance, performance portability, Programming Languages, Tesla K40

May 23, 2016 by hgpu

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi

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Tags: Computer science, CUDA, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Z

May 23, 2016 by hgpu

The Hitchhiker’s Guide to Cross-Platform OpenCL Application Development

Tyler Sorensen, Alastair F. Donaldson

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Tags: AMD Radeon R7 260, AMD Radeon R9 290X, ATI, Benchmarking, Computer science, Intel HD 5500, nVidia, nVidia GeForce GTX 980, nVidia Quadro K500, OpenCL

May 21, 2016 by hgpu

Architecture-Adaptive Code Variant Tuning

Saurav Muralidharan, Amit Roy, Mary Hall, Michael Garland, Piyush Rai

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 750, nVidia GeForce GTX 770, nVidia GeForce GTX 980, Package, Performance, Tesla C2075, Tesla K20

May 21, 2016 by hgpu

Performance Evaluation of Parallel Count Sort using GPU Computing with CUDA

Neetu Faujdar, SatyaPrakash Ghrera

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Sorting

May 21, 2016 by hgpu

GPU-based Pedestrian Detection for Autonomous Driving

Victor Campmany Canes

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 960, nVidia Tegra TX1, Vehicular traffic

May 21, 2016 by hgpu

Employing Directive Based Compression Solutions on Accelerators Global Memory under OpenACC

Ebad Salehi

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Tags: Compression, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Tesla K20, Thesis

May 21, 2016 by hgpu

GPU-Accelerated Feature Tracking

Alex Graves

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 780, OpenCL

May 17, 2016 by hgpu

DeepLearningKit – an GPU Optimized Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Amund Tveit, Torbjorn Morland, Thomas Brox Rost

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

May 17, 2016 by hgpu

A Foray into Efficient Mapping of Algorithms to Hardware Platforms on Heterogeneous Systems

Oren Segal, Nasibeh Nasiri, Martin Margala

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Tags: Benchmarking, Computer science, FPGA, Heterogeneous systems, nVidia, OpenCL, Tesla K40

May 17, 2016 by hgpu

Attention-based NMT Models as Feature Functions in Phrase-based SMT

Marcin Junczys-Dowmunt, Tomasz Dwojak, Rico Sennrich

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Tags: Algorithms, Computer science, CUDA, Machine learning, NLP, nVidia, nVidia GeForce GTX 970

May 17, 2016 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

Applications

Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks

ImageCL: An Image Processing Language for Performance Portability on Heterogeneous Systems

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

The Hitchhiker’s Guide to Cross-Platform OpenCL Application Development

Architecture-Adaptive Code Variant Tuning

Performance Evaluation of Parallel Count Sort using GPU Computing with CUDA

GPU-based Pedestrian Detection for Autonomous Driving

Employing Directive Based Compression Solutions on Accelerators Global Memory under OpenACC

GPU-Accelerated Feature Tracking

DeepLearningKit – an GPU Optimized Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

A Foray into Efficient Mapping of Algorithms to Hardware Platforms on Heterogeneous Systems

Attention-based NMT Models as Feature Functions in Phrase-based SMT

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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