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GLDA: GPU-Accelerated Latent Dirichlet Allocation Training

INTRODUCTION

GLDA (GPU-accelerated LDA) is an improved version based on the popular Latent Dirichlet Allocation (LDA) software GibbsLDA++: http://gibbslda.sourceforge.net/ The same user interfaces are adopted in GLDA. However, the training speed is singificantly improved. For the current version with one GPU used, a speedup of around 15X is achieved compared with the original CPU-based GibbsLDA++.

INSTALL

Please modify the Makefile to specific the compute capability of your NVIDIA GPU cards. Then just type: make

Then an executable file lda is generated in the directory.

CITATION

If you would like cite this work:

Mian Lu, Ge Bai, Qiong Luo, Jie Tang, Jiuxin Zhao. Accelerating Topic Model Training on a Single Machine. APWeb 2013: 184-195

CONTACT

Mian Lu [email protected]

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Contributors

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glda's Issues

cuda out of memory error

./src/CUDASampling.cpp(189) : CUDA Runtime API error 2: out of memory.

I used 3 million sentences to train a lda model with 512 topics.

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