A proposal for parallel self-adjusting computation

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.

• Type: Conference
• Year: 2007
• Where: POPL
• Authors: Matthew Hammer, Umut A. Acar, Mohan Rajagopalan, Anwar M. Ghuloum
• Keywords: Change Propagation Algorithm, POPL 2007, Programming Languages, Self-adjusting Computation, Sequential Execution Model