Sparse Tensor Support



MXNet v0.12 adds support for sparse tensors to efficiently store and compute tensors allowing developers to perform sparse matrix operations in a storage and compute-efficient manner and train deep learning models faster. This release supports two major sparse data formats–Compressed Sparse Row (CSR) and Row Sparse (RSP). The CSR format is optimized to represent matrices with large number of columns where each row has only a few non-zero elements. The RSP format is optimized to represent matrices with huge number of rows where most of the row slices are completely zeros. This release enables sparse support on CPU for most commonly used operators such as matrix dot product and element-wise operators. Sparse support for more operators will be added in future releases.