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Hardware Acceleration for Unstructured Big Data and Natural Language Processing

Prateek Tandon

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Tags: ASIC, big data, Computer science, FPGA, Hadoop, hardware acceleration, Machine learning, MapReduce, NLP, Thesis

February 2, 2016 by hgpu

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

Brian2 Atlas

A Unified Intermediate Representation and Execution Architecture for Heterogeneous and Distributed Neural Simulation

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelLens: Deploying Triton Kernels to C++ Inference Engines

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

Automated Instruction Encoding Synthesis for Modern GPU ISA Compression

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high performance computing on graphics processing units: hgpu.org

Hardware Acceleration for Unstructured Big Data and Natural Language Processing

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

Recent source codes

Brian2 Atlas

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

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