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CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Xiaolong Xie, Yun Liang, Xiuhong Li, Wei Tan

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Tags: Algorithms, Computer science, CUDA, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Package, Tesla V100

March 17, 2018 by hgpu

SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs

Kaiwei Li, Jianfei Chen, Wenguang Chen, Jun Zhu

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Tags: Algorithms, Computer science, CUDA, Information Retrieval, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X

October 12, 2016 by hgpu

Using Butterfly-Patterned Partial Sums to Optimize GPU Memory Accesses for Drawing from Discrete Distributions

Guy L. Steele Jr. (Oracle Labs), Jean-Baptiste Tristan

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Tags: Computer science, CUDA, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce Titan

May 16, 2015 by hgpu

Accelerating Topic Model Training on a Single Machine

Mian Lu, Ge Bai, Qiong Luo, Jie Tang, Jiuxin Zhao

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Tags: Computer science, CUDA, Information Retrieval, Latent Dirichlet allocation, nVidia, nVidia GeForce GTX 280

January 12, 2013 by hgpu

Accelerating Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation with Nvidia CUDA Compatible Devices

Tomonari Masada, Tsuyoshi Hamada, Yuichiro Shibata, Kiyoshi Oguri

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Tags: Computer science, CUDA, Latent Dirichlet allocation, nVidia, nVidia GeForce GTX 260

November 17, 2010 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs

Using Butterfly-Patterned Partial Sums to Optimize GPU Memory Accesses for Drawing from Discrete Distributions

Accelerating Topic Model Training on a Single Machine

Accelerating Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation with Nvidia CUDA Compatible Devices

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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