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Type Inference for Sublinear Space Functional Programming

Authors: DAL LAGO, UGO; U. Schoepp;

Type Inference for Sublinear Space Functional Programming

Abstract

We consider programming language aspects of algorithms that operate on data too large to fit into memory. In previous work we have introduced Intml, a functional programming language with primitives that support the implementation of such algorithms. We have shown that Intml can express all logspace functions but have left open the question how easy it is in practice to program typical logspace algorithms in Intml. In this paper we develop algorithms for Intml type inference. We show that with type inference one can handle programs that could not be reasonably manipulated by hand. We do so by implementing in intml a typical logspace algorithm, a test for acyclicity of undirected graphs. Thus we show that with type inference Intml can express typical algorithmic patterns of logspace easily and in a natural way.

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Italy
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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