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.

Advances in the development of parallel algorithms and system software now enable the ever-increasing power of scalable high-performance computers to be harnessed for scientific computing applications at fidelities that rival – and in many cases exceed – experimental methodologies.

This comprehensive text/reference, inspired by the visionary work of Prof. Ahmed H. Sameh, represents the state of the art in parallel numerical algorithms, applications, architectures, and system software. Articles in this collection address solutions to various challenges arising from concurrency, scale, energy efficiency, and programmability. These solutions are discussed in the context of diverse applications, ranging from scientific simulations to large-scale data analysis and mining.

Topics and features:

• Includes contributions from an international selection of world-class authorities, inspired by the work of Prof. Ahmed H. Sameh
• Examines various aspects of parallel algorithm-architecture interaction through articles on 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

This authoritative reference is a must-read for researchers and graduate students in disciplines as diverse as computational fluid dynamics, signal processing, and structural mechanics. Professionals involved in applications that rely on high-performance computers will also find the text an essential resource.