On Compact Mappings for Multicore Systems

Goens, Andrés; Menard, Christian; Castrillón, Jerónimo

doi:10.1007/978-3-030-27562-4_23

Cited by 2 publications

(1 citation statement)

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“…In previous work we have considered the geometry explicitly [12] but did not apply it to design-space exploration. Similarly, in [13] we discussed some geometric aspects of Network on Chip (NoC)-based architectures. This paper can be seen as an extension on the geometric considerations in these previous works.…”

Section: Related Workmentioning

confidence: 99%

Embeddings of Task Mappings to Multicore Systems

Goens

Castrillón

2022

Lecture Notes in Computer Science

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The problem of finding good mappings is central to designing and executing applications efficiently in embedded systems. In heterogeneous multicores, which are ubiquitous today, this problem yields an intractably large design space of possible mappings. Most methods explore this space using heuristics, many of which implicitly use geometric notions in mappings. In this paper we explore the geometry of the mapping problem explicitly, for finding embeddings of the mapping space that capture its structure. This allows us to formulate new mapping strategies by leveraging the geometry of the mapping space, as well as improving existing heuristics that do so implicitly. We evaluate our approach on a novel mapping heuristic based on gradient descent, as well as multiple existing meta-heuristics. For complex architectures, our methods improved the results of established exploration meta-heuristics by about an order of magnitude in average.

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Section: Related Workmentioning

confidence: 99%