Robust Mapping of Process Networks to Many-Core Systems using Bio-Inspired Design Centering

Hempel, Gerald; Goens, Andrés; Castrillón, Jerónimo; Asmus, Josefine; Sbalzarini, Ivo F.

doi:10.1145/3078659.3078667

Cited by 3 publications

(2 citation statements)

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“…In this paper we focus on the mapping problem addressed in these systems. As mentioned in the introduction, many such mapping algorithms implicitly use geometric structures of the mapping space [18,22,15,9,24]. These approaches do not explicitly model and reason about the geometry of the mapping space, this is done in an ad-hoc fashion.…”

Section: Related Workmentioning

confidence: 99%

“…For example, the Tabu Search algorithm proposed in [18] relies on exploring neighboring mappings in order to improve their performance. Other similar principles underly methods like Simulated Annealing [22], L p -adaptation [15] or genetic algorithms [9,24]. This is usually done in an adhoc fashion, without explicitly considering how to best endow the mapping space with such a geometric notion.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Embeddings of Task Mappings to Multicore Systems

Goens

Castrillón

2022

Lecture Notes in Computer Science

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Symmetry in Software Synthesis

Goens

Siccha

Castrillón

2017

ACM Trans. Archit. Code Optim.

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With the surge of multi- and manycores, much research has focused on algorithms for mapping and scheduling on these complex platforms. Large classes of these algorithms face scalability problems. This is why diverse methods are commonly used for reducing the search space. While most such approaches leverage the inherent symmetry of architectures and applications, they do it in a problem-specific and intuitive way. However, intuitive approaches become impractical with growing hardware complexity, like Network-on-Chip interconnect or heterogeneous cores. In this paper, we present a formal framework that can determine the inherent symmetry of architectures and applications algorithmically and leverage these for problems in software synthesis. Our approach is based on the mathematical theory of groups and a generalization called inverse semigroups. We evaluate our approach in two state-of-the-art mapping frameworks. Even for the platforms with a handful of cores of today and moderate-size benchmarks, our approach consistently yields reductions of the overall execution time of algorithms, accelerating them by a factor up to 10 in our experiments, or improving the quality of the results.Comment: 31 pages, 18 figure

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