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 …

We present a new randomized method for computing the min-plus product (aka, tropical
product) of two n× n matrices, yielding a faster algorithm for solving the all-pairs shortest …

Basic Liner algebra subprograms (BLAS) is a fundamental library in scientific computing. In
this paper, we present a template-based optimization framework, AUGEM, which can …

In this work, we use algebraic methods for studying distance computation and subgraph
detection tasks in the congested clique model. Specifically, we adapt parallel matrix …

Communication-optimal algorithms are known for square matrix multiplication. Here, we
obtain the first communication-optimal algorithm for all dimensions of rectangular matrices …

Matrix multiplication is a fundamental computation in many scientific disciplines. In this
paper, we show that novel fast matrix multiplication algorithms can significantly outperform …

The communication cost of algorithms (also known as I/O-complexity) is shown to be closely
related to the expansion properties of the corresponding computation graphs. We …

Parallel algorithms for sparse matrix-matrix multiplication typically spend most of their time
on inter-processor communication rather than on computation, and hardware trends predict …

Matrix multiplication is one of the most extensively used kernels in scientific computing.
Although subcubic algorithms exist, most high performance implementations are based on …

Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs.
The large data sizes of graphs and their vertex features make scalable training algorithms …