The traditional metric for the efficiency of a numerical algorithm has been the number of
arithmetic operations it performs. Technological trends have long been reducing the time to …

Abstract Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network
(DNN) training strategy, due to its universal applicability and its amenability to Single …

General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from
researchers in graph analyzing, scientific computing, and deep learning. Many optimization …

The GraphBLAS standard (GraphBlas. org) is being developed to bring the potential of
matrix-based graph algorithms to the broadest possible audience. Mathematically, the …

Efficient parallel multiplication of sparse matrices is key to enabling many large-scale
calculations. This article presents the DBCSR (Distributed Block Compressed Sparse Row) …

Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …

Triangle counting and enumeration are important kernels that are used to characterize
graphs. They are also used to compute important statistics such as clustering coefficients …

General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for
numerous applications such as algebraic multigrid method, breadth first search and shortest …

The Jaccard similarity index is an important measure of the overlap of two sets, widely used
in machine learning, computational genomics, information retrieval, and many other areas …

Sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high-
performance graph algorithms as well as for some linear solvers, such as algebraic …