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HPC User Forum, April 2008 John Hesterberg

HPC OS Directions and Requirements. HPC User Forum, April 2008 John Hesterberg. Processor-rich environments. Today's max SGI system sizes: 1000s of cores per ccNUMA Single System Image (SSI) ‏ 2048 -> 4096 cores limited by Linux scalability 1000s of small SSI nodes per system 14336 cores

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HPC User Forum, April 2008 John Hesterberg

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  1. HPC OS Directions and Requirements HPC User Forum, April 2008 John Hesterberg

  2. Processor-rich environments • Today's max SGI system sizes: • 1000s of cores per ccNUMA Single System Image (SSI)‏ • 2048 -> 4096 cores • limited by Linux scalability • 1000s of small SSI nodes per system • 14336 cores • limited by cost • Mutli-core is another layer of NUMA! In one SSI: • threads in a core • cores in a cpu/socket • sockets on a numa (or bus) node • numa nodes in a SSI • latency/bandwidth between nodes • SSIs in a system

  3. Processor-rich environments • Job placement, scheduling continue to be critical, both within SSI and across SSIs • batch managers need to see topology • placement policies based on applications • within a node: • processor/cache/node affinity • cpusets • isolated cpus • across nodes: • synchronized scheduling

  4. Resiliancy requirements • all hardware is not created equal. :)‏ ... but all hardware will fail • tolerate as many failures as possible • memory in particular • diskless clusters eliminate one more failure component • this is an ongoing (and endless) effort • tight integration between software and hardware helps • as systems scale, node failures become a certainty • application based checkpoint/restart? • OS supported checkpoint/restart?

  5. The Power of Linux • Disruptive OS development process • >1000 developers, 186 known companies (2.6.24)‏ • https://www.linux-foundation.org/publications/linuxkerneldevelopment.php • That's just the kernel!!! • Code reviews and signoffs • Hard and controversial changes get reviewed and done right • Open Source Flexibility • Can be customized for HPC hardware • Can be on the cutting edge of research • Productized and supported • Same OS on laptop, desktop, clusters with 1000s of nodes, SSIs with 1000s of cores • No vendor lock-in.

  6. Virtualization • Microkernel approaches (e.g. Xen)‏ • hides hardware details :-( • how much memory do you want to waste? • how much cpu do you want to waste? • what about IO performance? • KVM more interesting for some problems • First domain runs directly on hardware • Additional domains for compatibility. • Rebooting • Can be better for clusters with dedicated nodes...compare boot times to job runtimes • No added runtime overhead • Provides clean image • Multiple boot images on disk or on server • Integrate into batch manager!

  7. Green Computing • hardware is helping and hurting... • laptop capabilities moving up • more and larger systems consuming more power • HPC challenge: save power w/o sacrificing performance • tickless OS • cpu frequency management • management of idle cpus, nodes, and systems

  8. Slide 8

  9. OS Noise Synchronization‏ Unsynchronized OS Noise → Wasted Cycles Process on: Wasted Cycles Wasted Cycles System Overhead Node 1 Node 2 Compute Cycles Wasted Cycles System Overhead Wasted Cycles Node 3 System Overhead Wasted Cycles Wasted Cycles Barrier Complete Process on: System Overhead Node 1 System Overhead Node 2 System Overhead Node 3 Synchronized OS Noise → Faster Results Time • OS system noise: CPU cycles stolen from a user application by the OS to do periodic or asynchronous work (monitoring, daemons, garbage collection, etc). • Management interface will allow users to select what gets synchronized • Huge performance boost on larger scales systems Slide 9

  10. Servers: One Size Doesn’t Fit All! Focus on Efficiency Focus on Innovation • Workflow Characteristics • Price-performance key • Little data sharing • Predictable Workloads • Non-interactive • Standard Modes • Mature Apps • Workflow Characteristics • Multi-Discipline • Data-Intensive • Mixed or Uncertain Workloads • Interactivity • Rapid development cycles Capability & Conceptual Computing Capacity & Compliant Computing Slide 10

  11. User Productivity Advantages of Large Global Shared Memory Freedom to arbitrarily scale problem size without decomposition or other rework Minimal penalty to fetch off-node data Ability to freely exploit Open MP and/or MPI in any combination or scale. Simplified code development and prototyping platform Freedom to experiment without the hindrance of cluster paradigms Unified parallel C translator in development Greatly simplified load balancing Simple to direct a task to any processor as all data is accessible

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