High-Performance Computing
Paradigm and Infrastructure
Wiley Series on Parallel and Distributed Computing
1. Auflage Dezember 2005
816 Seiten, Hardcover
Wiley & Sons Ltd
Kurzbeschreibung
The coming years will witness a proliferation in the use of parallel and distributed computers. With hyperthreading in Intel processors, hypertransport links in next generation AMD processors, multi-core silicon in today's high-end microprocessors from IBM and emerging grid computing, parallel and distributed computers have moved into the mainstream. To fully exploit these advances in computer architecture, researchers must start to write parallel or distributed software and algorithms. The purpose of this title is to allow computer scientists, computational scientists and engineers, applied mathematicians, and researchers in other science and engineering disciplines to present, discuss, and exchange their recent advances, new ideas, results, works-in-progress, and experiences in the areas of parallel and distributed computing for science and engineering applications.
The state of the art of high-performance computing
Prominent researchers from around the world have gathered to present the state-of-the-art techniques and innovations in high-performance computing (HPC), including:
* Programming models for parallel computing: graph-oriented programming (GOP), OpenMP, the stages and transformation (SAT) approach, the bulk-synchronous parallel (BSP) model, Message Passing Interface (MPI), and Cilk
* Architectural and system support, featuring the code tiling compiler technique, the MigThread application-level migration and checkpointing package, the new prefetching scheme of atomicity, a new "receiver makes right" data conversion method, and lessons learned from applying reconfigurable computing to HPC
* Scheduling and resource management issues with heterogeneous systems, bus saturation effects on SMPs, genetic algorithms for distributed computing, and novel task-scheduling algorithms
* Clusters and grid computing: design requirements, grid middleware, distributed virtual machines, data grid services and performance-boosting techniques, security issues, and open issues
* Peer-to-peer computing (P2P) including the proposed search mechanism of hybrid periodical flooding (HPF) and routing protocols for improved routing performance
* Wireless and mobile computing, featuring discussions of implementing the Gateway Location Register (GLR) concept in 3G cellular networks, maximizing network longevity, and comparisons of QoS-aware scatternet scheduling algorithms
* High-performance applications including partitioners, running Bag-of-Tasks applications on grids, using low-cost clusters to meet high-demand applications, and advanced convergent architectures and protocols
High-Performance Computing: Paradigm and Infrastructure is an invaluable compendium for engineers, IT professionals, and researchers and students of computer science and applied mathematics.
Contributors.
PART 1. PROGRAMMING MODEL.
1. ClusterGOP: A High-Level Programming Environment for Clusters.
2. The Challenge of Providing A High-Level Programming Model for High-Performance Computing.
3. SAT: Toward Structured Parallelism Using Skeletons.
4. Bulk-Synchronous Parallelism: An Emerging Paradigm of High-Performance Computing.
5. Cilk Versus MPI: Comparing Two Parallel Programming Styles on Heterogenous Systems.
6. Nested Parallelism and Pipelining in OpenMP.
7. OpenMP for Chip Multiprocessors.
PART 2. ARCHITECTURAL AND SYSTEM SUPPORT.
8. Compiler and Run-Time Parallelization Techniques for Scientific Computations on Distributed-Memory Parallel Computers.
9. Enabling Partial-Cache Line Prefetching Through Data Compression.
10. MPI Atomicity and Concurrent Overlapping I/O.
11. Code Tiling: One Size Fits All.
12. Data Conversion for Heterogeneous Migration/Checkpointing.
13. Receiving-Message Prediction and Its Speculative Execution.
14. An Investigation of the Applicability of Distributed FPGAs to High-Performance Computing.
PART 3. SCHEDULING AND RESOURCE MANAGEMENT.
15. Bandwidth-Aware Resource Allocation for Heterogeneous Computing Systems to Maximize Throughput.
16. Scheduling Algorithms with Bus Bandwidth Considerations for SMPs.
17. Toward Performance Guarantee of Dynamic Task Scheduling of a Parameter-Sweep Application onto a Computational Grid.
18. Performance Study of Reliability Maximization and Turnaround Minimization with GA-based Task Allocation in DCS.
19. Toward Fast and Efficient Compile-Time Task Scheduling in Heterogeneous Computing Systems.
20. An On-Line Approach for Classifying and Extracting Application Behavior on Linux.
PART 4. CLUSTERS AND GRID COMPUTING.
21. Peer-to-Peer Grid Computing and a .NET-Based Alchemi Framework.
22. Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies.
23. High-Performance Computing on Clusters: The Distributed JVM Approach.
24. Data Grids: Supporting Data-Intensive Applications in Wide-Area Networks.
25. Application I/O on a Parallel File System for Linux Clusters.
26. One Teraflop Achieved with a Geographically Distributed Linux Cluster.
27. A Grid-Based Distributed Simulation of Plasma Turbulence.
28. Evidence-Aware Trust Model for Dynamic Services.
PART 5. PEER-TO-PEER COMPUTING.
29. Resource Discovery in Peer-to-Peer Infrastructures.
30. Hybrid Periodical Flooding in Unstructured Peer-to-Peer Networks.
31. HIERAS: A DHT-Based Hierarchical P2P Routing Algorithm.
32. Flexible and Scalable Group Communication Model for Peer-to-Peer Systems.
PART 6. WIRELESS AND MOBILE COMPUTING.
33. Study of Cache-Enhanced Dynamic Movement-Based Location Management Schemes for 3G Cellular Networks.
33. 1 Introduction.
34. Maximizing Multicast Lifetime in Wireless Ad Hoc Networks.
35. A QoS-Aware Scheduling Algorithm for Bluetooth Scatternets.
PART 7. HIGH PERFORMANCE APPLICATIONS.
36. A Workload Partitioner for Heterogeneous Grids.
37. Building a User-Level Grid for Bag-of-Tasks Applications.
38. An Efficient Parallel Method for Calculating the Smarandache Function.
39. Design, Implementation and Deployment of a Commodity Cluster for Peirodic Comparison of Gene Sequences.
40. A Hierarchical Distributed Shared-Memory Parallel Branch & Bound Application with PVM and OpenMP on Multiprocessor Clusters.
41. IP Based Telecommunication Services.
Index.
MINYI GUO received his PhD from the University of Tsukuba, Japan. He is currently an Associate Professor in the Department of Computer Software at the University of Aizu, Japan. In addition, Dr. Guo is Editor in Chief of the International Journal of Embedded Systems, and has written and edited books in the area of parallel and distributed computing, as well as embedded and ubiquitous computing.
