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Automatic Tuning of Local Memory Use on GPGPUs

Tianyi David Han, Tarek S. Abdelrahman

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Tags: Benchmarking, Computer science, Machine learning, nVidia, OpenCL, Performance, Tesla M2090

December 26, 2014 by hgpu

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, Yann LeCun

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Tags: Algorithms, CNN, Computer science, CUDA, FFT, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, Package, Tesla K40

December 26, 2014 by hgpu

Purine: A bi-graph based deep learning framework

Min Lin, Shuo Li, Xuan Luo, Shuicheng Yan

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Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia

December 22, 2014 by hgpu

Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification

Hongsheng Li, Rui Zhao, Xiaogang Wang

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Tags: Algorithms, CNN, Computer science, Computer vision, CUDA, Machine learning, Neural networks, nVidia, Tesla K40

December 16, 2014 by hgpu

MatConvNet – Convolutional Neural Networks for MATLAB

Andrea Vedaldi, Karel Lenc

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Tags: Computer science, CUDA, Machine learning, Mathematical Software, Neural and Evolutionary Computing, Neural networks, nVidia

December 16, 2014 by hgpu

Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning

Minjie Wang, Tianjun Xiao, Jianpeng Li, Jiaxing Zhang, Chuntao Hong, Zheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

December 15, 2014 by hgpu

Graph Processing on GPU

Zhang Jingbo

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Tags: Algorithms, Computer science, CUDA, Data mining, Graph theory, Machine learning, nVidia, nVidia GeForce 9400 GT, nVidia GeForce GT 330, nVidia GeForce GT 520, Thesis

December 14, 2014 by hgpu

Real-Time Grasp Detection Using Convolutional Neural Networks

Joseph Redmon, Anelia Angelova

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Tags: CNN, Computer science, CUDA, Machine learning, Neural networks, nVidia, Tesla K20

December 12, 2014 by hgpu

Theano-based Large-Scale Visual Recognition with Multiple GPUs

Weiguang Ding, Ruoyan Wang, Fei Mao, Graham Taylor

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Tags: Computer science, CUDA, Data parallelism, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Package, Python

December 9, 2014 by hgpu

MLitB: Machine Learning in the Browser

Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling

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Tags: Algorithms, Computer science, Distributed computing, Heterogeneous systems, Java, Machine learning, Neural networks, Package

December 9, 2014 by hgpu

Neural Networks through Shared Maps in Mobile Devices

William Raveane, Maria Angelica Gonzalez Arrieta

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Tags: Computer science, Computer vision, Image recognition, Machine learning, Neural networks, OpenGL

December 7, 2014 by hgpu

An Approach for Maximizing Performance on Heterogeneous Clusters of CPU and GPU

Nam-Luc Tran, Sabri Skhiri, Arnaud Schils, Edgar Isaac Hiroshi Leon Saiki

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan, Package

December 2, 2014 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

Automatic Tuning of Local Memory Use on GPGPUs

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Purine: A bi-graph based deep learning framework

Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification

MatConvNet – Convolutional Neural Networks for MATLAB

Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning

Graph Processing on GPU

Real-Time Grasp Detection Using Convolutional Neural Networks

Theano-based Large-Scale Visual Recognition with Multiple GPUs

MLitB: Machine Learning in the Browser

Neural Networks through Shared Maps in Mobile Devices

An Approach for Maximizing Performance on Heterogeneous Clusters of CPU and GPU

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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