Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Analysing Dataflow Programs with Causation Traces

Boulasikis, Michail LU ; Gruian, Flavius LU orcid ; Callanan, Gareth LU and Janneck, Jörn W. LU (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:
author
; ; and
organization
publishing date
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}},
}