Distributed and Parallel Systems: Cluster and Grid Computing

最新推荐文章于 2024-09-09 14:56:08 发布
原创 最新推荐文章于 2024-09-09 14:56:08 发布 · 672 阅读 · 0 ·
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#parallel #tools #出版

本文介绍了一系列研究主题，分为四个主要部分，涵盖了集群工具和技术的介绍，以及负载均衡等问题。
版权声明：原创作品，允许转载，转载时请务必以超链接形式标明文章原始出版、作者信息和本声明。否则将追究法律责任。 http://blog.youkuaiyun.com/topmvp - topmvp
The papers in this volume cover a broad range of research topics presented in four groups, introducing cluster tools and techniques, and the issues of load balancing.
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Springer - Distributed and Parallel Systems - Cluster and Grid Computing.rar
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Springer 出版的《Distributed and Parallel Systems - Cluster and Grid Computing》一书深入探讨了这两个主题，提供了丰富的理论知识和技术实践。 在分布式系统中，多个独立的计算机节点通过网络连接，共同完成一...
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本书"Cluster & Grid computing"可能会探讨以下内容： 1. 分布式与并行计算的基础理论和架构。 2. 集群计算的硬件设计和软件配置。 3. 网格计算的架构模型，如OGF（开放网格服务架构）。 4. 负载均衡、容错和故障...
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grid cluster
最新发布
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### Grid Cluster in Computing Architecture and Implementation Grid clusters represent an advanced form of distributed computing infrastructure designed to provide high-performance processing capabilities through interconnected nodes that work together as a single system. The architecture of grid clusters is built on the principles outlined for scalable systems such as Mercury's interface components which include network abstraction layers (NAL), remote procedure call interfaces (RPCs), and bulk data transfer mechanisms[^1]. However, while these elements are crucial for understanding how individual parts communicate within a cluster, they do not fully capture what makes grids unique. In contrast with traditional supercomputers or even tightly coupled parallel machines, grid clusters emphasize resource sharing across geographically dispersed locations. This approach allows users from different organizations to pool their computational resources into one virtual organization without requiring physical colocation. Such flexibility comes at the cost of increased complexity when managing heterogeneous hardware configurations but offers significant advantages regarding fault tolerance and load balancing. The core concept behind implementing a grid cluster involves creating middleware software layers responsible for abstracting away differences between underlying platforms so applications can run seamlessly over diverse environments. These abstractions typically involve: - **Resource Management Systems**: Tools like Condor or Torque manage job scheduling policies ensuring efficient allocation based upon availability metrics. - **Data Access Services**: Protocols enabling secure file transfers among participating sites via protocols including FTPS/HTTPS/WebDAV etc., alongside caching strategies aimed at reducing latency during I/O operations. - **Security Frameworks**: Mechanisms providing authentication & authorization services necessary for protecting sensitive information exchanged throughout the federation. To illustrate this further using code snippets relevant to setting up basic communication channels within Python-based projects could look something along those lines below where `mpi4py` library facilitates message passing interface functionality allowing processes running independently yet collaboratively perform tasks assigned collectively by master node(s): ```python from mpi4py import MPI comm = MPI.COMM_WORLD rank = comm.Get_rank() if rank == 0: data = {'a': 7, 'b': 3.14} else: data = None data = comm.bcast(data, root=0) print(f"Process {rank} received data: ", data) ``` This example demonstrates broadcasting messages containing dictionaries holding arbitrary key-value pairs originating from process zero outwards towards all other members forming part of our hypothetical mini-grid setup.
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