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Past and current research in computer performance analysis has focused primarily on dedicated parallel machines. However, future applications in the area of high-performance computing will not only use individual parallel systems but a large set of networked resources. This scenario of computational and data Grids is attracting a great deal of attention from both computer and computational scientists. In addition to the inherent complexity of parallel machines, the sharing and transparency of the available resources introduces new challenges on performance analysis, techniques, and systems. In order to meet those challenges, a multi-disciplinary approach to the multi-faceted problems of performance is required. New degrees of freedom will come into play with a direct impact on the performance of Grid computing, including wide-area network performance, quality-of-service (QoS), heterogeneity, and middleware systems, to mention only a few.
List of contents
Different Approaches to Automatic Performance Analysis of Distributed Applications.- Performance Modeling of Deterministic Transport Computations.- Performance Optimization of RK Methods Using Block-based Pipelining.- Performance Evaluation of Hybrid Parallel Programming Paradigms.- Performance Modelling for Task-Parallel Programs.- Collective Communication Patterns on the Quadrics Network.- The Design of a Performance Steering System for Component-based Grid Applications.- Advances in the TAU Performance System.- Uniform Resource Visualization: Software and Services.- A Performance Analysis Tool for Interactive Grid Applications.- Dynamic Instrumentation for Java Using a Virtual JVM.- Aksum: A Performance Analysis Tool for Parallel and Distributed Applications.- Commercial Applications of Grid Computing.- Mesh Generation and Optimistic Computation on the Grid.- Grid Performance and Resource Management using Mobile Agents.- Monitoring of Interactive Grid Applications.- The UNICORE Grid and Its Options for Performance Analysis.
Summary
Past and current research in computer performance analysis has focused primarily on dedicated parallel machines. However, future applications in the area of high-performance computing will not only use individual parallel systems but a large set of networked resources. This scenario of computational and data Grids is attracting a great deal of attention from both computer and computational scientists. In addition to the inherent complexity of parallel machines, the sharing and transparency of the available resources introduces new challenges on performance analysis, techniques, and systems. In order to meet those challenges, a multi-disciplinary approach to the multi-faceted problems of performance is required. New degrees of freedom will come into play with a direct impact on the performance of Grid computing, including wide-area network performance, quality-of-service (QoS), heterogeneity, and middleware systems, to mention only a few.
