Wilkinson defined a sparse matrix as one with enough zeros that it pays to take advantage of
them. 1 This informal yet practical definition captures the essence of the goal of direct …

The subject of sparse matrices has its root in such diverse fields as management science,
power systems analysis, surveying, circuit theory, and structural analysis. Efficient use of …

We are witnessing a dramatic change in computer architecture due to the multicore
paradigm shift, as every electronic device from cell phones to supercomputers confronts …

This paper introduces a storage format for sparse matrices, called compressed sparse
blocks (CSB), which allows both Ax and A, x to be computed efficiently in parallel, where A is …

Krylov subspace methods (KSMs) are iterative algorithms for solving large, sparse linear
systems and eigenvalue problems. Current KSMs rely on sparse matrix-vector multiply …

Hypergraphs are a generalization of graphs where edges (aka nets) are allowed to connect
more than two vertices. They have a similarly wide range of applications as graphs. This …

Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

Graph partitioning is often used for load balancing in parallel computing, but it is known that
hypergraph partitioning has several advantages. First, hypergraphs more accurately model …

This is the first text explaining how to use the bulk synchronous parallel (BSP) model and the
freely available BSPlib communication library in parallel algorithm design and parallel …

Network embedding, which learns low-dimensional vector representation for nodes in the
network, has attracted considerable research attention recently. However, the existing …