A proposal for parallel self-adjusting computation

  • Authors:
  • Matthew Hammer;Umut A. Acar;Mohan Rajagopalan;Anwar Ghuloum

  • Affiliations:
  • Toyota Technological Institute, Chicago, IL;Toyota Technological Institute, Chicago, IL;Programming Systems Lab, Intel, Santa Clara, CA;Programming Systems Lab, Intel, Santa Clara, CA

  • Venue:
  • Proceedings of the 2007 workshop on Declarative aspects of multicore programming
  • Year:
  • 2007

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Abstract

We present an overview of our ongoing work on parallelizing self-adjusting-computation techniques. In self-adjusting computation, programs can respond to changes to their data (e.g., inputs, outcomes of comparisons) automatically by running a change-propagation algorithm. This ability is important in applications where inputs change slowly over time. All previously proposed self-adjusting computation techniques assume a sequential execution model. We describe techniques for writing parallel self-adjusting programs and a change propagation algorithm that can update computations in parallel. We describe a prototype implementation and present preliminary experimental results.