Michael W. Berry

Yousef Saad

Kyle A. Gallivan

Efstratios Gallopoulos

Ananth Grama

Bernard Philippe

Faisal Saied

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.

Algorithms and Applications

High-Performance Scientific Computing

Sanjay Ranka

Rajkumar Buyya

Vir V. Phoha

Rajeev Kumar

Yeh-Ching Chung

Srinivas Aluru

Sumeet Dua

Sandeep Gupta

This book constitutes the refereed papers of the 2nd International Conference on Contemporary Computing, which was held in Noida (New Delhi), India, in August 2009. The 61 revised full papers presented were carefully reviewed and selected from 213 submissions and focus on topics that are of contemporary interest to computer and computational scientists and engineers. The papers are organized in topical sections on Algorithms, Applications, Bioinformatics, and Systems.

Second International Conference, IC3 2009, Noida, India, August 17-19, 2009. Proceedings

Contemporary Computing

Ahmed H. Sameh

This contributed volume highlights two areas of fundamental interest in high-performance computing: core algorithms for important kernels and computationally demanding applications. The first few chapters explore algorithms, numerical techniques, and their parallel formulations for a variety of kernels that arise in applications. The rest of the volume focuses on state-of-the-art applications from diverse domains. By structuring the volume around these two areas, it presents a comprehensive view of the application landscape for high-performance computing, while also enabling readers to develop new applications using the kernels. Readers will learn how to choose the most suitable parallel algorithms for any given application, ensuring that theory and practicality are clearly connected. Applications using these techniques are illustrated in detail, including: Computational materials science and engineering Computational cardiovascular analysis Multiscale analysis of wind turbines and turbomachinery Weather forecasting Machine learning techniques Parallel Algorithms in Computational Science and Engineering will be an ideal reference for applied mathematicians, engineers, computer scientists, and other researchers who utilize high-performance computing in their work.

Parallel Algorithms in Computational Science and Engineering

Mehmet Koyutürk

Shankar Subramaniam

Molecular networks provide descriptions of the organization of various biological processes, including cellular signaling, metabolism, and genetic regulation. Knowledge on molecular networks is commonly used for systems level analysis of biological function; research and method development in this area has grown tremendously in the past few years. This book will provide a detailed review of existing knowledge on the functional characterization of biological networks. In 15 chapters authored by an international group of prolific systems biology and bioinformatics researchers, it will organize, conceptualize, and summarize the existing core of research results and computational methods on understanding biological function from a network perspective.

Functional Coherence of Molecular Networks in Bioinformatics

Ananth Grama

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