MagmaDNN
Daniel Nichols, Kwai Lam Wong, Stanimire Z. Tomov, Lucien K. L. Ng, Sihan Chen, Alexander Gessinger · Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) · 2019
MagmaDNN [17] is a deep learning framework driven using the highly optimized MAGMA dense linear algebra package. The library offers comparable performance to other popular frameworks, such as TensorFlow, PyTorch, and Theano. C++ is used to implement the framework providing fast memory operations, direct cuda access, and compile time errors. Common neural network layers such as Fully Connected, Convolutional, Pooling, Flatten, and Dropout are included. Hyperparameter tuning is performed with a parallel grid search engine.