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- W4221093999 abstract "Dataflow languages have, as well as functional languages, immutable semantics, which is often implemented by copying values. A common compiler optimization known from functional languages involves analyzing which data structures can be modified in-place instead of copying them. This paper presents a novel algorithm to this so called Aggregate Update Problem for multi-clocked dataflow languages, i.e. those that allow streams to have events at disjoint timestamps, like e.g. Lucid, Lustre and Signal. Unrestricted multi-clocked languages require a static triggering analysis on how events and hence data values are read, written and replicated. We use TeSSLa as a generic stream transformation language with a small set of operators to develop our ideas. We implemented the solution in a TeSSLa compiler targeting the Java VM via Scala code generation which combines persistent data structures and mutable data structures for those data values which allow in-place editing. Our empirical evaluation shows considerable speedup for use cases where queues, maps or sets are dominant data structures." @default.
- W4221093999 created "2022-04-03" @default.
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- W4221093999 date "2022-04-02" @default.
- W4221093999 modified "2023-09-25" @default.
- W4221093999 title "Aggregate Update Problem for Multi-clocked Dataflow Languages" @default.
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- W4221093999 doi "https://doi.org/10.1109/cgo53902.2022.9741275" @default.
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