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Maximizing Parallelism for Future Machines: MCMD Workshop at Salish Lodge, Jan 24, 2007

Explore multilevel parallelism in applications for teraflop and petaflop-scale systems, focusing on MCMD model extensions and collaborations.

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Maximizing Parallelism for Future Machines: MCMD Workshop at Salish Lodge, Jan 24, 2007

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Presentation Transcript

  1. Multiple Component Multiple DataMini Workshop Salish Lodge January 24, 2007

  2. Multilevel Parallelism • How can applications effectively exploit the massive amount of parallelism available in teraflop and future petaflop-scale machines? • Massive numbers of CPUs in future systems require algorithm and software redesidgn to exploit all available parallelism • A proven solution is • To exploit fully available hardware parallelism for applications, exploitation of multiple levels of parallelism (MLP) • Hierarchical parallelism – algorithm decomposition at different levels • Increases granularity of computation => improve the overall scalability.

  3. Multiple Component Multiple Data • MCMD extends the SCMD (single component multiple data) model that was the main focus of CCA in Scidac-1 • Prototype solution described at SC’05 for computational chemistry • Allows different groups of processors execute different CCA components • Main motivation is support for multiple levels of parallelism in applications

  4. Issues • Investigate requirements in existing and emerging CCA applications • Define extensions to the CCA specs? • Processor group management in CCA • Enhancements to the CCA core software stack (frameworks, services) • MCMD-awareness in the scientific component toolkit • Context of different programming models (MPI, PVM, GA, OpenMP, Pthreads, GAS languages, DARPA HPCS languages) • Relationship and collaboration with other CCA activities like CQoS, CCA on hybrid platforms, fault tolerance • External collaborations and synergistic activities elsewhere

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