We consider multiphysics applications from algorithmic and architectural perspectives,
where “algorithmic” includes both mathematical analysis and computational complexity, and …

This book offers a comprehensive presentation of some of the most successful and popular
domain decomposition preconditioners for finite and spectral element approximations of …

To better inform the subsurface scientist on the expected performance of parallel simulators,
this work investigates performance of the reactive multiphase flow and multicomponent …

This paper looks at the evolution of the" Memory Wall" problem over the past decade. It
begins by reviewing the short Computer Architecture News note that coined the phrase …

These notes serve as an introduction to a subject of study in computational mathematics
referred to as domain decomposition methods. It concerns divide and conquer methods for …

Large-scale optimization of systems governed by partial differential equations (PDEs) is a
frontier problem in scientific computation. Reduced quasi-Newton sequential quadratic …

The Sparse Matrix-Vector multiplication (SpMV) kernel scales poorly on shared memory
systems with multiple processing units due to the streaming nature of its data access pattern …

The performance of scienti c computing applications often achieves only a small fraction of
peak performance 7, 17]. In this paper, we discuss two causes of performance problems …

Previous research work has identified memory bandwidth as the main bottleneck of the
ubiquitous Sparse Matrix-Vector Multiplication kernel. To attack this problem, we aim at …

In this article, we present a parallel geometric multigrid algorithm for solving variable-
coefficient elliptic partial differential equations on the unit box (with Dirichlet or Neumann …