TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Executing dataflow actors as kahn processes

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2015 Proceedings of the International Conference on Embedded Software, EMSOFT 2015
KustantajaInstitute of Electrical and Electronics Engineers Inc.
Sivut105-114
Sivumäärä10
ISBN (elektroninen)9781467380799
DOI - pysyväislinkit
TilaJulkaistu - 4 marraskuuta 2015
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma13th International Conference on Embedded Software, EMSOFT 2015 - Amsterdam, Alankomaat
Kesto: 4 lokakuuta 20159 lokakuuta 2015

Conference

Conference13th International Conference on Embedded Software, EMSOFT 2015
MaaAlankomaat
KaupunkiAmsterdam
Ajanjakso4/10/159/10/15

Tiivistelmä

Programming models which specify an application as a network of independent computational elements have emerged as a promising paradigm for programming streaming applications. The antagonism between expressivity and analysability has led to a number of different such programming models, which provide different degrees of freedom to the programmer. One example are Kahn process networks (KPNs), which, due to certain restrictions in communication, can guarantee determinacy (their results are independent of timing by construction). On the other hand, certain dataflow models, such as the CAL Actor Language, allow non-determinacy and thus higher expressivity, however at the price of static analysability and thus a potentially less efficient implementation. In many cases, however, non-determinacy is not required (or even not desired), and relying on KPN for the implementation seems advantageous. In this paper, we propose an algorithm for classifying dataflow actors (i.e. computational elements) as KPN compatible or potentially not. For KPN compatible dataflow actors, we propose an automatic KPN translation method based on this algorithm. In experiments, we show that more than 75% of all mature actors of a standard multimedia benchmark suite can be classified as KPN compatible and that their execution time can be reduced by up to 1.97x using our proposed translation technique. Finally, in a manual classification effort, we validate these results and list different classes of KPN incompatibility.

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