Published on 25 July 2016
ISBN-10: 0738441872
ISBN-13: 9780738441870
IBM Form #: SG24-8280-00
Authors: Dino Quintero, Luis Carlos Cruz Huertas, Tsuyoshi Kamenoue, Wainer dos Santos Moschetta, Mauricio Faria de Oliveira, Georgy E Pavlov and Alexander Pozdneev
This IBM® Redbooks® publication demonstrates and documents that IBM Power Systems™ high-performance computing and technical computing solutions deliver faster time to value with powerful solutions. Configurable into highly scalable Linux clusters, Power Systems offer extreme performance for demanding workloads such as genomics, finance, computational chemistry, oil and gas exploration, and high-performance data analytics.
This book delivers a high-performance computing solution implemented on the IBM Power System S822LC. The solution delivers high application performance and throughput based on its built-for-big-data architecture that incorporates IBM POWER8® processors, tightly coupled Field Programmable Gate Arrays (FPGAs) and accelerators, and faster I/O by using Coherent Accelerator Processor Interface (CAPI). This solution is ideal for clients that need more processing power while simultaneously increasing workload density and reducing datacenter floor space requirements. The Power S822LC offers a modular design to scale from a single rack to hundreds, simplicity of ordering, and a strong innovation roadmap for graphics processing units (GPUs).
This publication is targeted toward technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) responsible for delivering cost effective high-performance computing (HPC) solutions that help uncover insights from their data so they can optimize business results, product development, and scientific discoveries
Chapter 1. Introduction to the IBM Power System S822LC for high performance computing workloads
Chapter 2. Reference architecture
Chapter 3. Hardware components
Chapter 4. Software stack
Chapter 5. Software deployment
Chapter 6. Application development and tuning
Chapter 7. Running applications
Chapter 8. Cluster monitoring
Appendix A. Applications and performance