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Extracting Flow Features Using Bag-of-Features and Supervised Learning Techniques

Yifei Li

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Tags: Algorithms, Clustering, Computer science, Computer vision, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, OpenMP, Package, Thesis, Visualization

February 2, 2016 by hgpu

Deep learning review and its applications

Andoni Azkarate Saiz

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 660, Review, Thesis

February 1, 2016 by hgpu

GPU-Accelerated Recurrent Neural Networks: OpenCLLink and SymbolicC

Zdenek Buk

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Tags: Computer science, Deep learning, Machine learning, Neural networks, nVidia, OpenCL, Package, RNN

January 29, 2016 by hgpu

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Francisco Javier Ordonez, Daniel Roggen

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Tags: Computer science, Deep learning, LSTM, Machine learning, Neural networks, Package, Python, RNN, Theano

January 26, 2016 by hgpu

Adaptive and Hybrid Machine Learning Approaches Utilizing General Purpose Computing on Graphical Processing Units

Mohammed Abdelrahim

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Tags: Computer science, CUDA, Heterogeneous systems, LSTM, Machine learning, nVidia, nVidia GeForce GTX 770, OpenMP, Thesis

January 19, 2016 by hgpu

Classification of Higgs Boson Tau-Tau decays using GPU accelerated Neural Networks

Mohit Shridhar

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Tags: CUDA, Deep learning, High Energy Physics - Phenomenology, Machine learning, Neural networks, nVidia, Physics

January 14, 2016 by hgpu

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

Amund Tvei, Torbjorn Morland, Thomas Brox Rost

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

January 7, 2016 by hgpu

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

Joseph D. Garvey

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Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation, Thesis

January 4, 2016 by hgpu

A Deep Generative Deconvolutional Image Model

Yunchen Pu, Xin Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin

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Tags: Bayesian, Caffe, Computer science, Computer vision, CUDA, Deep learning, Image generation, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

December 31, 2015 by hgpu

Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

Hao Zhang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Gunhee Kim, Qirong Ho, Eric Xing

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Tags: Caffe, Computer science, Computer vision, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, Package, Tesla K20

December 23, 2015 by hgpu

Autotuning Stencils Codes with Algorithmic Skeletons

Chris Cummins

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenCL, Package

December 19, 2015 by hgpu

Origami: A Convolutional Network Accelerator

Lukas Cavigelli, David Gschwend, Christoph Mayer, Samuel Willi, Beat Muheim, Luca Benini

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Tags: Algorithms, Artificial intelligence, ASIC, Computer science, Computer vision, Machine learning, Neural networks

December 15, 2015 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Extracting Flow Features Using Bag-of-Features and Supervised Learning Techniques

Deep learning review and its applications

GPU-Accelerated Recurrent Neural Networks: OpenCLLink and SymbolicC

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Adaptive and Hybrid Machine Learning Approaches Utilizing General Purpose Computing on Graphical Processing Units

Classification of Higgs Boson Tau-Tau decays using GPU accelerated Neural Networks

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

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

A Deep Generative Deconvolutional Image Model

Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

Autotuning Stencils Codes with Algorithmic Skeletons

Origami: A Convolutional Network Accelerator

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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