Scaling irregular applications on commodity clusters

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Irregular applications are hard to scale

Irregular applications arise in:

Low locality and frequent random communication make these applications difficult to scale on commodity clusters.

Grappa makes scaling irregular applications easier

Programming models like MapReduce and GraphLab can be restrictive; for example, parallel recursive algorithms are hard to express.

Grappa provides a familiar multithreaded programming model, making it easy to scale up irregular applications on commodity clusters.

Grappa's core features

Grappa’s runtime system consists of three key components:
Tasking layer
Supports millions of threads across the cluster
Automatically balances load with work stealing

Distributed shared memory (DSM)
Provides a single system, shared memory abstraction

Supports high-throughput small message communication

About the project

Grappa is a project group in the Sampa Group at the University of Washington.

Graduate students
Jacob Nelson
Brandon Myers
Brandon Holt
Simon Kahan
Preston Briggs
Luis Ceze
Mark Oskin

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