paper-ParallelPython-Short/sections/conclusions.tex

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We introduce a hybrid ``incremental'' approach to tracking dependencies through computational notebooks, and implement a scheduler based on this approach in an ICE-architecture notebook.
This incremental provenance model allows for (i) parallel cell execution, and (ii) automatic refresh of dependent cells after modifications.
We note several opportunities for improvement on our initial proof-of-concept, most notably to reduce blocking due to state transfer between kernels.
For example, an advanced scheduler could dynamically trade off between the overheads of inter-kernel communication, and the benefits of parallel execution.
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