This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …

We identify the challenges that are special to parallel sparse matrix-matrix multiplication
(PSpGEMM). We show that sparse algorithms are not as scalable as their dense …

Traditionally distributed graph processing systems have largely focused on scalability
through the optimizations of inter-node communication and load balance. However, they …

Tensor algebra is a powerful tool with applications in machine learning, data analytics,
engineering and the physical sciences. Tensors are often sparse and compound operations …

Sparse matrices are widely used in graph and data analytics, machine learning, engineering
and scientific applications. This paper describes and analyzes OuterSPACE, an accelerator …

Given the growing importance of large-scale graph analytics, there is a need to improve the
performance of graph analysis frameworks without compromising on productivity. GraphMat …

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

Phenomenally successful in practical inference problems, convolutional neural networks
(CNN) are widely deployed in mobile devices, data centers, and even supercomputers. The …

SuiteSparse: GraphBLAS is a full implementation of the GraphBLAS standard, which defines
a set of sparse matrix operations on an extended algebra of semirings using an almost …

Sparse tensors are rapidly becoming critical components of modern deep learning
workloads. However, developing high-performance sparse operators can be difficult and …