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Cluster Success Measures for Distributed Supercomputers

Explore the best measures of success for central distributed supercomputer systems. Learn about potential users and strategies to encourage adoption. Discuss user classifications and examples of early adopters and conservative users in the EPFL cluster community.

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Cluster Success Measures for Distributed Supercomputers

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  1. An application-based measureof cluster success Mark L. SawleyVisiting Scientist: EPF-Lausanne, SwitzerlandPermanent address: CSIRO Mathematical & Information Sciences, Australia Presented at the 4th Workshop on Distributed Supercomputers, New Orleans, March 9-10, 2000

  2. Measure of cluster success Question: What is the best measure of success of a general-purpose central distributed supercomputer (cluster) system? (One) Answer: A wide application community, not just a small number of heavy users This measure is very stringent and is comprised of more conventional aspects, such as performance and availability.

  3. Who are the potential users? • An abbreviated history of central parallel machines at the EPFL: • 1986 Cray 1 (2 proc.) • 1988 Cray 2 (4 proc.) • 1994 Cray Y-MP (4 proc.) + Cray T3D (256 proc.) • 1996 SGI Origin2000 (80 proc.) • 1998  Swiss Tx (8  70 proc.) • Potential EPFL cluster community origins: • vector (Crays, NEC) • distributed memory massively parallel (Cray T3D) • NUMA (SGI Origin2000) • workstation

  4. Classification of users Require “early adapters” to encourage use by “conservative users” otherwise number of users will be limited.

  5. “Technology enthusiasts” • Characteristics: • not interested in application results • main interest in future technology • Needs: • a system (containing hardware + software) • performance irrelevant • no additional software required • access to system gurus • sufficient time (between machine crashes)

  6. “Early adapters” • Characteristics: • interested in application results • willing to invest in future technology • Needs: • robust system (hardware + software) • good performance • minimum amount of software (MPI, BLAS, ….) • basic level of user support • dedicated time (for benchmarking)

  7. “Conservative users” • Characteristics: • only interested in application results • not willing to invest in future technology • Needs: • user-friendly system (looks like presently-used system) • excellent performance (better than presently-used system) • extensive software (debugging, visualisation, commercial codes, …) • high level of user support • sufficient machine access (to complete large-scale applications)

  8. Examples of “early adapters” of the Swiss-T1 • Computational Fluid Dynamics– nsmb (multi-block, finite volume)– fluent (unstructured, finite volume) • –speculoos (object-oriented, spectral element) • Granular flow • – gfc (discrete element method) • Molecular Dynamics • –lautrec (Carr-Parrinello method) • Structural analysis • – crash (object-oriented, finite element) • Plasma transport • – orb (particle-in-cell, finite element)

  9. Examples of “conservative users” • Two classes: • Users porting their own codes– need a lot of user support • Users of existing parallel codes • – in-house • – commercial • At the EPFL, these users can potentially come from any of the • 11 different departments (ranging from Mathematics to Architecture). • Both classes can be encouraged by benchmark results.

  10. Example of a commercial code benchmarks Fluent CFD code: Suite of 9 different test cases of different size and physical complexity (eg turbulence, combustion) Small class < 100,000 cells Medium class 100,000 – 500,000 cells Large class > 500,000 cells Full details: www.fluent.com/software/fl5bench

  11. Computational Fluid Dynamics (nsmb) Code: nsmb is a 2D/3D multi-block compressible flow solver Personnel: Jan Vos (EPFL)

  12. Computational Fluid Dynamics (nsmb) AS28G full aircraft configuration, using mesh: 62 unequal blocks 3.5 million cells (~ 2 GB memory required)

  13. Computational Fluid Dynamics (fluent) Code: fluent is a commercial 2D/3D unstructured flow solver Personnel: Mark Sawley (EPFL/CSIRO) Kenics static mixer

  14. Computational Fluid Dynamics (fluent)

  15. Granular flow (gfc) Code: gfc is a 2D/3D Discrete Element Method code Personnel: Mark Sawley & Paul Cleary (CSIRO) Hicom nutating mill Flow from a two-port hopper

  16. Granular flow (gfc)

  17. Molecular Dynamics (lautrec) Code: lautrec is a first-principles MD code using Carr-Parrinello method Personnel: Alessandro De Vita, Massimiliano Stengel (EPFL) Andrew Canning, Roland Richter (SGI/Cray Research) DNA molecule diamond-graphite transition bovine pancreatic trypsin inhibitor

  18. Molecular Dynamics (lautrec) Performance results on Swiss-T1 baby machine using MPI on T-Net. big system 32 water molecues cell dimension: 18.64 a.u. cut-off energy: 90 Ry FFT grid: 112x112x112 medium system 32 water molecules cell dimension: 18.64 a.u. cut-off energy: 30 Ry FFT grid: 64x64x64 small system 108 aluminium atoms cell dimension: 22.92 a.u. cut-off energy: 10 Ry FFT grid: 48x48x48

  19. Conclusion of preliminary benchmarks • The initial timing results of application codes on the Swiss-T1 show: • good overall performance due to Alpha ev6 processor • good scalability up to the 12 processors available on the prototype Swiss-T1 baby • substantially better scalability for communication-intensive applications using T-Net compared to Fast- or Giga-Ethernet

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