Informing Static Mapping and Local Scheduling of Stream Programs with Trace Analysis
(2023) 25th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2023 p.98-103- Abstract
- Due to their natural and inherent way of capturing concurrency, dataflow descriptions of stream programs have seen prevalent usage in fields such as video processing, networks and scientific computing. They are often deployed on manycore, heterogeneous and distributed architectures. Despite robust research on the topic, obstacles still exist in evaluating the performance of stream programs accurately, especially without a complete implementation down to the selected platforms. In this work we introduce and provide a proof of concept for an automated design space exploration flow where causation traces and simulation are used to inform the mapping and scheduling of stream programs running on distributed platforms, using only high-level... (More)
- Due to their natural and inherent way of capturing concurrency, dataflow descriptions of stream programs have seen prevalent usage in fields such as video processing, networks and scientific computing. They are often deployed on manycore, heterogeneous and distributed architectures. Despite robust research on the topic, obstacles still exist in evaluating the performance of stream programs accurately, especially without a complete implementation down to the selected platforms. In this work we introduce and provide a proof of concept for an automated design space exploration flow where causation traces and simulation are used to inform the mapping and scheduling of stream programs running on distributed platforms, using only high-level models of the architecture. The basic idea behind the flow is to profile the designs under well performing mappings and schedules very early in the design process to accurately gauge performance potential. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4f2ba978-1cae-4306-bb93-6a696081547b
- author
- Boulasikis, Michail LU ; Gruian, Flavius LU ; Callanan, Gareth LU and Janneck, Jörn LU
- organization
- publishing date
- 2023-09
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Dataflow, Actor Networks, Hetereogeneous Architectures, Embedded Systems
- host publication
- Informing Static Mapping and Local Scheduling of Stream Programs with Trace Analysis
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 25th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2023
- conference location
- Nancy, France
- conference dates
- 2023-09-11 - 2023-09-14
- external identifiers
-
- scopus:85193825953
- ISBN
- 979-8-3503-9412-2
- DOI
- 10.1109/SYNASC61333.2023.00021
- project
- Employing AI Hardware for General Purpose Computing
- language
- English
- LU publication?
- yes
- id
- 4f2ba978-1cae-4306-bb93-6a696081547b
- date added to LUP
- 2024-05-13 14:28:23
- date last changed
- 2024-06-19 14:31:53
@inproceedings{4f2ba978-1cae-4306-bb93-6a696081547b, abstract = {{Due to their natural and inherent way of capturing concurrency, dataflow descriptions of stream programs have seen prevalent usage in fields such as video processing, networks and scientific computing. They are often deployed on manycore, heterogeneous and distributed architectures. Despite robust research on the topic, obstacles still exist in evaluating the performance of stream programs accurately, especially without a complete implementation down to the selected platforms. In this work we introduce and provide a proof of concept for an automated design space exploration flow where causation traces and simulation are used to inform the mapping and scheduling of stream programs running on distributed platforms, using only high-level models of the architecture. The basic idea behind the flow is to profile the designs under well performing mappings and schedules very early in the design process to accurately gauge performance potential.}}, author = {{Boulasikis, Michail and Gruian, Flavius and Callanan, Gareth and Janneck, Jörn}}, booktitle = {{Informing Static Mapping and Local Scheduling of Stream Programs with Trace Analysis}}, isbn = {{979-8-3503-9412-2}}, keywords = {{Dataflow; Actor Networks; Hetereogeneous Architectures; Embedded Systems}}, language = {{eng}}, pages = {{98--103}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Informing Static Mapping and Local Scheduling of Stream Programs with Trace Analysis}}, url = {{http://dx.doi.org/10.1109/SYNASC61333.2023.00021}}, doi = {{10.1109/SYNASC61333.2023.00021}}, year = {{2023}}, }