MaTEx

"MaTEx is a collection of parallel machine learning and data mining (MLDM) algorithms, targeted for desktops, supercomputers and cloud computing systems. MaTEx provides a handful of widely used algorithms in Clustering, Classification and Association Rule Mining (ARM).

MaTEx primarily provides high performance implementations of Deep Learning algorithms. The current implementations use MPI for inter-node communication and multi-threading/CUDA (cuDNN) for intra-node execution, by using Google TensorFlow as the baseline.

MaTEx also supports K-means, Spectral Clustering algorithms for Clustering, Support Vector Machines, KNN algorithms for Classification, and FP-Growth for Association Rule Mining.

MaTEx uses state-of-the-art programming models such as Message Passing Interface (MPI), CUDA and multi-threading models for targeting massively parallel systems readily available on modern desktops, supercomputers and cloud computing systems.

The required software such as mpich-3.1 is bundled with MaTEx. These package are automatically built, if they are not found on your system.

https://github.com/abhinavvishnu/matex/wiki