15 lines
1.1 KiB
TeX
15 lines
1.1 KiB
TeX
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We introduce a provenance-based approach for predicting and tracking dependencies across python cells in a computational notebook and an implementation of this approach in Vizier, a data-centric notebook system where cells are isolated from each other and communicate through data artifacts. By combining best effort static analysis with an adaptable runtime schedule for notebook cell execution, we achieve (i) parallel execution of python cells, (ii) automatic refresh of dependent cells when the notebook is modified, and (iii) translation of Jupyter notebooks into our model.
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This paper represents an initial proof-of-concept, on which we note several opportunities for improvement.
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Crucially, there are still considerable opportunities to reduce blocking due to state transfer between kernels.
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For example, it may be possible to re-use a kernel for applications involving large state, trading the increased overhead of sequential execution for a reduction in overhead from state transfer.
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We also plan to explore ways to make state export and import asynchronous.
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