Analysing Dataflow Programs with Causation Traces
(2022) 31st International Conference on Parallel Architectures and Compilation Techniques, PACT 2022 p.534-535- Abstract
Stream processing applications are naturally described as dataflow programs. Dataflow programs modelled as actor networks are well suited to describe concurrent and computationally intensive problems. Realistic dataflow programs are typically characterized by highly dynamic behaviour, limiting the applicability of static analysis techniques. In this work we explore using dynamic analyses of dataflow programs by making use of causation traces; graphs which capture instances of the program's execution. We outline how they can be used to inform pipelining and architectural decisions and conclude by delineating how this research can be expanded upon using multiple traces and doing more types of analyses.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3b22fb0d-a3bf-4a13-8d5a-8e134d166442
- author
- Boulasikis, Michail LU ; Gruian, Flavius LU ; Callanan, Gareth LU and Janneck, Jörn W. LU
- organization
- publishing date
- 2022-10
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the 2022 International Conference on Parallel Architectures and Compilation Techniques
- pages
- 2 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 31st International Conference on Parallel Architectures and Compilation Techniques, PACT 2022
- conference location
- Chicago, United States
- conference dates
- 2022-10-08 - 2022-10-10
- external identifiers
-
- scopus:85147328566
- ISBN
- 9781450398688
- DOI
- 10.1145/3559009.3569660
- project
- Employing AI Hardware for General Purpose Computing
- language
- English
- LU publication?
- yes
- id
- 3b22fb0d-a3bf-4a13-8d5a-8e134d166442
- date added to LUP
- 2023-02-20 14:00:27
- date last changed
- 2023-11-21 06:50:58
@inproceedings{3b22fb0d-a3bf-4a13-8d5a-8e134d166442, abstract = {{<p>Stream processing applications are naturally described as dataflow programs. Dataflow programs modelled as actor networks are well suited to describe concurrent and computationally intensive problems. Realistic dataflow programs are typically characterized by highly dynamic behaviour, limiting the applicability of static analysis techniques. In this work we explore using dynamic analyses of dataflow programs by making use of causation traces; graphs which capture instances of the program's execution. We outline how they can be used to inform pipelining and architectural decisions and conclude by delineating how this research can be expanded upon using multiple traces and doing more types of analyses.</p>}}, author = {{Boulasikis, Michail and Gruian, Flavius and Callanan, Gareth and Janneck, Jörn W.}}, booktitle = {{Proceedings of the 2022 International Conference on Parallel Architectures and Compilation Techniques}}, isbn = {{9781450398688}}, language = {{eng}}, pages = {{534--535}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Analysing Dataflow Programs with Causation Traces}}, url = {{http://dx.doi.org/10.1145/3559009.3569660}}, doi = {{10.1145/3559009.3569660}}, year = {{2022}}, }