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Massively Deep Artificial Neural Networks for Handwritten Digit Recognition

Keiron O'Shea

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 580

July 24, 2015 by hgpu

On the use of deep Boltzmann machines for road signs classification

Carlo D'Eramo

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Tags: Computer science, Computer vision, CUDA, Image recognition, Machine learning, nVidia, Python, Tesla C1060, Thesis

July 22, 2015 by hgpu

Adaptive parallelism mapping in dynamic environments using machine learning

Murali Krishna Emani

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Tags: Computer science, Machine learning, OpenMP, Performance, Thesis

July 15, 2015 by hgpu

Towards Good Practices for Very Deep Two-Stream ConvNets

Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, MPI, nVidia, OpenMPI, Package

July 10, 2015 by hgpu

High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Anton Akusok, Kaj-Mikael Bjork, Yoan Miche, Amaury Lendasse

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, OpenCL, Package, Python

July 6, 2015 by hgpu

Parallel Sparse Coding for Seafloor Image Analysis

Genlang Chen, Chenggang Lai, Miaoqing Huang

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Tags: CUDA, Image processing, Intel Xeon Phi, Machine learning, nVidia, Tesla K20

July 3, 2015 by hgpu

Heterogeneous Network Embedding via Deep Architectures

Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang

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Tags: Computer science, CUDA, Data mining, Deep learning, Machine learning, nVidia, Tesla K40

July 1, 2015 by hgpu

Alpha-Beta Divergences Discover Micro and Macro Structures in Data

Karthik Narayan, Ali Punjani, Pieter Abbeel

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Tags: Computer science, Computer vision, CUDA, Machine learning, nVidia, Tesla K40, Visualization

June 24, 2015 by hgpu

Generating Efficient Tensor Contractions for GPUs

Thomas Nelson, Axel Rivera, Prasanna Balaprakash, Mary Hall, Paul D. Hovland, Elizabeth Jessup, Boyana Norris

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Tags: Algorithms, Chemistry, Computer science, CUDA, Fluid dynamics, Machine learning, nVidia, OpenACC, Optimization

June 22, 2015 by hgpu

Automatic Selection of Sparse Matrix Representation on GPUs

Naser Sedaghati, Te Mu, Louis-Noel Pouchet, Srinivasan Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, Physics, Sparse matrix, Tesla K20, Tesla K40

June 14, 2015 by hgpu

CGO: G: Intelligent Heuristic Construction with Active Learning

William F. Ogilvie, Pavlos Petoumenous, Zheng Wang, Hugh Leather

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Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Optimization

June 8, 2015 by hgpu

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla K40

June 7, 2015 by hgpu

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

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high performance computing on graphics processing units: hgpu.org

Massively Deep Artificial Neural Networks for Handwritten Digit Recognition

On the use of deep Boltzmann machines for road signs classification

Adaptive parallelism mapping in dynamic environments using machine learning

Towards Good Practices for Very Deep Two-Stream ConvNets

High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Parallel Sparse Coding for Seafloor Image Analysis

Heterogeneous Network Embedding via Deep Architectures

Alpha-Beta Divergences Discover Micro and Macro Structures in Data

Generating Efficient Tensor Contractions for GPUs

Automatic Selection of Sparse Matrix Representation on GPUs

CGO: G: Intelligent Heuristic Construction with Active Learning

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Recent source codes

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

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