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by Joanna Wong, Gheorghe Tudor, Daniel de Souza Casali, Dusan Smolej, Ricardo Macha
IBM Platform Computing Solutions Reference Architectures and Best Practices
Front cover
Notices
Trademarks
Preface
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Chapter 1. Introduction
1.1 Why IBM Platform Computing?
1.2 High performance clusters
1.3 IBM Platform HPC implementation scenario
1.4 Big Data implementation on an IBM high performance computing cluster
1.5 IBM Platform Computing solutions and products
Chapter 2. High performance clusters
2.1 Cluster management
2.1.1 IBM Platform HPC
2.1.2 IBM Platform Cluster Manager Standard Edition
2.1.3 IBM Platform Cluster Manager Advanced Edition
2.2 Workload management
2.2.1 IBM Platform Load Sharing Facility
2.2.2 IBM Platform Symphony
2.3 Reference architectures
2.3.1 IBM Application Ready Solution for Abaqus
2.3.2 IBM Application Ready Solution for Accelrys
2.3.3 IBM Application Ready Solution for ANSYS
2.3.4 IBM Application Ready Solution for CLC bio
2.3.5 IBM Application Ready Solution for Gaussian
2.3.6 IBM Application Ready Solution for InfoSphere BigInsights
2.3.7 IBM Application Ready Solution for mpiBLAST
2.3.8 IBM Application Ready Solution for MSC Software
2.3.9 IBM Application Ready Solution for Schlumberger
2.3.10 IBM Application Ready Solution for Technical Computing
2.3.11 IBM System x and Cluster Solutions configurator
2.4 Workload optimized systems
2.4.1 NeXtScale System
2.4.2 iDataPlex
2.4.3 Intelligent Cluster
2.4.4 Enterprise servers
2.4.5 High volume systems
Chapter 3. IBM Platform High Performance Computing implementation scenario
3.1 Application Ready Solution versus scenario-based implementation
3.2 Scenario-based implementation
3.2.1 Cluster hardware
3.2.2 Management nodes
3.2.3 Compute nodes
3.2.4 Networking
3.2.5 Cluster prerequisites
3.2.6 Cluster deployment
3.2.7 Provisioning cluster resources
3.2.8 IBM Platform HPC high availability
3.2.9 Running applications in Platform HPC
Chapter 4. IBM Big Data implementation on an IBM High Performance Computing cluster
4.1 IBM high performance computing for Big Data analytics reference architectures
4.2 High performance low latency Big Data solutions stack by using PCM-SE for a Platform Symphony MapReduce cluster
4.2.1 Installing IBM Platform Cluster Manager Standard Edition
4.2.2 Installing the IBM General Parallel File System
4.2.3 GPFS open source portability layer
4.2.4 Configuring GPFS on the cluster initial nodes
4.2.5 Hadoop installation process for the initial nodes
4.2.6 Installing IBM Platform Symphony
4.2.7 Building an automatic kit template
4.2.8 Adding the kit to an image profile
4.2.9 Testing a Platform Symphony MapReduce job
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Note: Before using this information and the product it supports, read the information in “Notices” on page v.
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