Scaling to New HeightsRetrospective

IEEE/ACM SC2002 Conference

Baltimore, MD

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, at PSC.

• 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.

The following slides represent a collection of ideas from the workshop

Basic Concepts

• All application components must scale• Control granularity; Virtualize• Incorporate latency tolerance• Reduce dependency on synchronization• Maintain per-process load; Facilitate balance

Only new aspect is the degree to which these things matter

Issues and Remedies

• Granularity

• Latencies

• Synchronization

• Load Balancing

• Heterogeneous Considerations

Granularity

Define problem in terms of a large number of small objects independent of the process count

• 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

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!

• Software Overheads– Can be more significant than network delays– NUMA architectures

Scalable designs must accommodate latencies

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?

Load Balancing

• Static load balancing– Reduces to granularity problem– Differences between processors and network

segments are determined a priori

• Dynamic process management requires distributed monitoring capabilities– Must be scalable– System maps objects to processes

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

Poor Scalability?

Processors

Speedup

Good Scalability?

Processors

Speedup

Processors

Speedup

Performance Comparison

Tools

• Automated 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 Lab– Develop performance models (machine profiles;

application signatures) and trending patterns

Identify/fix bottlenecks; choose new methods?

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

0.001

0.01

0.1

1

10

4 8 16 32 64 128 256 512 1024

4 procs/node

perfect

0.001

0.01

0.1

1

64 128 256 512 1024

4 procs/node

perfect

0.01

0.1

1

10

4 8 16 32 64 128 256 512 1024 2048

4 procs/node

perfect

0.001

0.01

0.1

1

128 256 512 1024 2048

4 procs/node

perfect

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

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

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?