Michael W. Berry & Kyle A. Gallivan 
High-Performance Scientific Computing 
Algorithms and Applications

This book presents the state of the art in parallel numerical algorithms, applications, architectures, and system software. The book examines various solutions for issues of concurrency, scale, energy efficiency, and programmability, which are discussed in the context of a diverse range of applications. Features: includes contributions from an international selection of world-class authorities; examines parallel algorithm-architecture interaction through issues of computational capacity-based codesign and automatic restructuring of programs using compilation techniques; reviews emerging applications of numerical methods in information retrieval and data mining; discusses the latest issues in dense and sparse matrix computations for modern high-performance systems, multicores, manycores and GPUs, and several perspectives on the Spike family of algorithms for solving linear systems; presents outstanding challenges and developing technologies, and puts these in their historical context.

Table of Content

Parallel Numerical Computing from Illiac IV to Exascale.- Computational Capacity-Based Co-design of Computer Systems.- Measuring Computer Performance.- A Compilation Framework for the Automatic Restructuring of Pointer-Linked Data Structures.- Dense Linear Algebra on Accelerated Multicore Hardware.- The Explicit SPIKE Algorithm.- The SPIKE Factorization as Domain Decomposition Method.- Parallel Solution of Sparse Linear Systems.- Parallel Block-Jacobi SVD Methods.- Robust and Efficient Multifrontal Solver for Large Discretized PDEs.- A Preconditioned Scheme for Nonsymmetric Saddle-Point Problems.- Effect of Ordering for Iterative Solvers in Structural Mechanics Problems.- Scaling Hypre’s Multigrid Solvers to 100, 000 Cores.- A Riemannian Dennis-Moré Condition.- A Jump-Start of Non-Negative Least Squares Solvers.- Fast Nonnegative Tensor Factorization with an Active-Set-Like Method.- Knowledge Discovery Using Nonnegative Tensor Factorization with Visual Analytics.