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Naiad: Iterative and Incremental Data-Parallelism using Differential Dataflow

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Naiad is a new framework for data-parallel computing, based on potentially-cyclic dataflow graphs. Naiad’s execution model is based on processing successive differences between collections, which naturally enables efficient support for iterative algorithms and programs where the inputs change incrementally. In this talk, I will motivate and introduce our “differential dataflow” model, and describe the language (NaiadLINQ) and prototypical multithreaded implementation (NaiadMP) that we have developed. We implemented our current proof of concept for a single shared-memory machine, and I will present some preliminary results from this setting. We are currently investigating how the Naiad model can be extended to a cluster setting, and I will discuss some of the challenges and trade-offs that we are facing in that regard.

This is joint work with Frank McSherry, Rebecca Isaacs and Michael Isard.

This talk is part of the Microsoft Research Cambridge, public talks series.

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