1. SOS7, Durango CO, 4-Mar-2003 Scaling to New Heights Retrospective IEEE/ACM SC2002 Conference Baltimore, MD Distilled [Trimmed & Distilled for SOS7 by M. Levine 4-March-2003, Durango]

2. SOS7, Durango CO, 4-Mar-2003 Contacts and References David O’Neal oneal@ncsa.uiuc.edu John Urbanic urbanic@psc.edu Sergiu Sanielevici sergiu@psc.edu Workshop materials: www.psc.edu/training/scaling/workshop.html

3. SOS7, Durango CO, 4-Mar-2003 Introduction More than 80 researchers from universities, research centers, and corporations around the country attended the first "Scaling to New Heights" workshop, May 20 and 21, 2002, at the PSC, Pittsburgh. Sponsored by the NSF leading-edge centers (NCSA, PSC, SDSC) together with the Center for Computational Sciences (ORNL) and NERSC, the workshop included a poster session, invited and contributed talks, and a panel. Participants examined issues involved in adapting and developing research software to effectively exploit systems comprised of thousands of processors. [Fred/Neil’s Q1.] The following slides represent a collection of ideas from the workshop

4. SOS7, Durango CO, 4-Mar-2003 Basic Concepts All application components must scaleAll application components must scale Control granularity; VirtualizeControl granularity; Virtualize Incorporate latency toleranceIncorporate latency tolerance Reduce dependency on synchronizationReduce dependency on synchronization Maintain per-process load; Facilitate balanceMaintain per-process load; Facilitate balance Only new aspect, at larger scale, is the degree to which these things matter

5. SOS7, Durango CO, 4-Mar-2003 Poor Scalability? (Keep your eye on the ball) Processors Speedup

6. SOS7, Durango CO, 4-Mar-2003 Good Scalability? (Keep your eye on the ball) Processors Speedup

7. SOS7, Durango CO, 4-Mar-2003 Processors Speedup Performance is the Goal! (Keep your eye on the ball)

8. SOS7, Durango CO, 4-Mar-2003 Issues and Remedies Granularity [Q2a] Latencies [Q2b] Synchronization Load Balancing [Q2c] Heterogeneous Considerations

9. SOS7, Durango CO, 4-Mar-2003 Granularity Define problem in terms of a large number of small objects independent of the process count [Q2a] Object design considerations –Caching and other local effects –Communication-to-computation ratio Control granularity through virtualization –Maintain per-process load level –Manage comms within virtual blocks, e.g. Converse –Facilitate dynamic load balancing

10. SOS7, Durango CO, 4-Mar-2003 Latencies Network –Latency reduction lags improvement in flop rates; Much easier to grow bandwidth –Overlap communications and computations; Pipeline larger messages –Don’t wait – Speculate! [Q2b] Software Overheads –Can be more significant than network delays –NUMA architectures Scalable designs must accommodate latencies

11. SOS7, Durango CO, 4-Mar-2003 Synchronization Cost increases with the process count –Synchronization doesn’t scale well –Latencies come into play here too Distributed resource exacerbates problems –Heterogeneity another significant obstacle Regular communication patterns are often characterized by many synchronizations –Best suited to homogeneous co-located clusters Transition to asynchronous models?

12. SOS7, Durango CO, 4-Mar-2003 Load Balancing Static load balancing –Reduces to granularity problem –Differences between processors and network segments are determined a priori Dynamic process management requiring distributed monitoring capabilities [Q2c]Dynamic process management requiring distributed monitoring capabilities [Q2c] –Must be scalable –System maps objects to processes

13. SOS7, Durango CO, 4-Mar-2003 Heterogeneous Considerations Similar but different processors or network components configured within a single cluster –Different clock rates, NICs, etc. Distinct processors, networking segments, and operating systems operating at a distance –Grid resources Elevates significance of dynamic load balancing; Data-driven objects immediately adaptable

14. SOS7, Durango CO, 4-Mar-2003 Tools [Q2d?] Automated algorithm selection and performance tuning by empirical means, e.g. ATLASAutomated algorithm selection and performance tuning by empirical means, e.g. ATLAS –Generate space of algorithms and search for fastest implementations by running them Scalability prediction, e.g. PMaC LabScalability prediction, e.g. PMaC Lab –Develop performance models (machine profiles; application signatures) and trending patterns Identify/fix bottlenecks; choose new methods?

15. SOS7, Durango CO, 4-Mar-2003 Topics for Discussion How should large, scalable computational science problems be posed? Should existing algorithms and codes be modified or should new ones be developed? Should agencies explicitly fund collaborations to develop industrial-strength, efficient, scalable codes? What should cyber-infrastructure builders and operators do to help scientists develop and run good applications?

16. SOS7, Durango CO, 4-Mar-2003 Summary Comments (MJL) Substantial progress, with scientific payoff, is being made. It is hard work without magic bullets. >>> Dynamic load balancing <<< –Big payoff, homogeneous and heterogeneous –Requires considerable people work to implement –Runtime overhead very small.

17. SOS7, Durango CO, 4-Mar-2003 Case Study: NAMD Scalable Molecular Dynamics Three-dimensional object-oriented code Message-driven execution capability Fixed problem sizes determined by biomolecular structures Embedded PME electrostatics processor Asynchronous communications

18. SOS7, Durango CO, 4-Mar-2003

22. Case Study: Summary As more processes are used to solve the given fixed-size problems, benchmark times decrease to a few milliseconds –PME communication times and operating system loads are significant in this range Scaling to many thousands of processes is almost certainly achievable now given a large enough problem –700 atoms/process x 3,000 processes = 2.1M atoms