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Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
Rev Up Your HPC Engine
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Rev Up Your HPC Engine

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In this slidecast, Fritz Ferstl from Univa presents: Rev Up Your HPC Engine. The presentation explores the challenges for Workload Management systems in today's datacenters with ever-increasing core …

In this slidecast, Fritz Ferstl from Univa presents: Rev Up Your HPC Engine. The presentation explores the challenges for Workload Management systems in today's datacenters with ever-increasing core counts.

See the presentation video and the full transcript: http://wp.me/p3RLHQ-cjs

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Transcript

  • 1. Rev Up Your HPC Engine Fritz Ferstl, CTO Univa Corp, fferstl@univa.com
  • 2. Who is Univa? Copyright © 2014 Univa Corporation. All Rights Reserved. 2 • Profile • Based in Chicago, global reach • >500 customers in 3 yrs (mostly Fortune 500) • Products /Technologies: • Univa Grid Engine • UniSight • Univa License Orchestrator • UniCloud Data Center Automation Experts Do more with less in Big Compute and Big Data Help organizations play a better game of Tetris
  • 3. Challenges for Workload and Resource Management Systems Copyright © 2014 Univa Corporation. All Rights Reserved. 3
  • 4. Scalability • Node counts stay flat or go down, sockets stay flat, cores explode • With the core explosion, the number of jobs also explodes • Ever shorter run-times, more applications, more use cases • Large commercial sites approach or go beyond 100K • Throughput clusters process >150 million jobs / month 4Copyright © 2014 Univa Corporation. All Rights Reserved.
  • 5. Heterogeneity 5Copyright © 2014 Univa Corporation. All Rights Reserved. • Hardware • Multi-sockets, multi-cores • Partial cluster upgrades • Evolving memory, network and storage architectures • Accelerators: GPUs, Phi • Job Profiles • Throughput • Array Jobs • Large Parallel • Interactive • Sessions • Reservations • Transactional • Hybrid • Dependencies, Workflows
  • 6. Policy Variety 6Copyright © 2014 Univa Corporation. All Rights Reserved. • Automated  Transparency? • Manual overrides • Preferential access • Priorities • Reservations • Resource Urgencies • Quotas • Deadlines • Conflict Resolution • E.g. don‘t starve large parallel plus maintain high utilization
  • 7. Use Case Variety 7Copyright © 2014 Univa Corporation. All Rights Reserved. • Classical HPC (simulation)  Large parallel / many mid-size parallel • Verification / Test  Throughput • From single simulation to parameter study  array jobs • Ultra-short jobs • Big Data / Data Mining • Exclusive usage of nodes vs shared usage
  • 8. Geographical Distribution / Clouds 8Copyright © 2014 Univa Corporation. All Rights Reserved. • Resource sharing: servers, licenses, data, other • Data access latencies • Security • File system dependencies • Pre-/Post-Staging • Data locality: • Bring the job to the data • Or bring the data to the job
  • 9. Solutions Approaches Best Practices Copyright © 2014 Univa Corporation. All Rights Reserved. 9
  • 10. Evolve • Architecture Evolution • more cores / nodes / jobs  make it faster • Integration with GPUs, Phi, etc • New Scheduling Algorithms • Efficient handling of job mixes: parallel / array / sequential jobs • Scheduling of ultra-short jobs • More Monitoring, Better Error Tracking • Reporting, Accounting & Analytics 10Copyright © 2014 Univa Corporation. All Rights Reserved.
  • 11. Be Street-Smart • Simplify where possible! • Be-all solution can be the most expensive • Effort • Poor utilization  slow ROI • Focus on most important goals 11Copyright © 2014 Univa Corporation. All Rights Reserved.
  • 12. Think Different • Examples: • Less HA @ more throughput via fast SSD-Raid with regular back-up • Use array jobs whereever possible • More smaller jobs vs fewer bigger jobs • All considered, preemption may be a good option 12Copyright © 2014 Univa Corporation. All Rights Reserved.
  • 13. Accept Difference • Simple: temporarily designate parts of cluster • Advanced: Cloud-share • Share resources across separate workload management system instances • Dynamically re-assign resources (servers) based on demand • Provides autonomy while maintaining high utilization • But avoid meta-scheduling where you can! 13Copyright © 2014 Univa Corporation. All Rights Reserved.
  • 14. Tailored Solutions • Tailoring & add-ons can make all the difference • Tailoring such as • Job Classes • Customized reports • Add-ons such as • Submission portals and wrappers 14Copyright © 2014 Univa Corporation. All Rights Reserved.
  • 15. Conclusions • Workload & Resource Management Systems more required than ever • Specifically in the “new” era of Cloud and Big Data • Allows you to benefit from 20+ years of experience in HPC workload orchestration and to move beyond • Clear-cut set of challenges  non-trivial solutions • Build on best-in-class products, architectures and development teams • Being “street-smart” about architecting and configuration of a cluster has big impact 15Copyright © 2014 Univa Corporation. All Rights Reserved.
  • 16. Thank You http://www.univa.com fferstl@univa.com Copyright © 2014 Univa Corporation. All Rights Reserved. 16

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