Tensor comprehensions in SaC

Sven-Bodo Scholz, Artjoms Šinkarovs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


We propose a new notation for data parallel operators on multi-dimensional arrays named tensor comprehensions. This notation combines the basic principle of array-comprehensions with syntactical shortcuts very close to those found in the so-called Tensor Notations used in Physics and Mathematics. As a result, complex operators with rich semantics can be defined concisely. The key to this conciseness lies in the ability to define shape-polymorphic operations combined with the ability to infer array shapes from the immediate context. The paper provides a definition of the proposed notation, a formal shape inference process, as well as a set of re-write rules that translates tensor comprehensions as a zero-cost syntactic sugar into standard SaC expressions.

Original languageEnglish
Title of host publicationProceedings of the 31st Symposium on Implementation and Application of Functional Languages
EditorsJurrien Stutterheim, Wei Ngan Chin
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450375627
Publication statusPublished - 25 Sept 2019
Event31st Symposium on Implementation and Application of Functional Languages 2019 - Singapore, Singapore
Duration: 25 Sept 201927 Sept 2019


Conference31st Symposium on Implementation and Application of Functional Languages 2019
Abbreviated titleIFL 2019

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software


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