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Model-Based Dynamic Scheduling for Multicore Signal Processing

Tutkimustuotosvertaisarvioitu

Standard

Model-Based Dynamic Scheduling for Multicore Signal Processing. / Wu, Jiahao; Blattner, Timothy; Keyrouz, Walid; Bhattacharyya, Shuvra S.

julkaisussa: Journal of Signal Processing Systems, 2018, s. 1-14.

Tutkimustuotosvertaisarvioitu

Harvard

Wu, J, Blattner, T, Keyrouz, W & Bhattacharyya, SS 2018, 'Model-Based Dynamic Scheduling for Multicore Signal Processing', Journal of Signal Processing Systems, Sivut 1-14. https://doi.org/10.1007/s11265-018-1412-5

APA

Wu, J., Blattner, T., Keyrouz, W., & Bhattacharyya, S. S. (2018). Model-Based Dynamic Scheduling for Multicore Signal Processing. Journal of Signal Processing Systems, 1-14. https://doi.org/10.1007/s11265-018-1412-5

Vancouver

Wu J, Blattner T, Keyrouz W, Bhattacharyya SS. Model-Based Dynamic Scheduling for Multicore Signal Processing. Journal of Signal Processing Systems. 2018;1-14. https://doi.org/10.1007/s11265-018-1412-5

Author

Wu, Jiahao ; Blattner, Timothy ; Keyrouz, Walid ; Bhattacharyya, Shuvra S. / Model-Based Dynamic Scheduling for Multicore Signal Processing. Julkaisussa: Journal of Signal Processing Systems. 2018 ; Sivut 1-14.

Bibtex - Lataa

@article{bb30d29bea6949ba96ddfe2d9865f240,
title = "Model-Based Dynamic Scheduling for Multicore Signal Processing",
abstract = "This paper presents a model-based design method and a corresponding new software tool, the HTGS Model-Based Engine (HMBE), for designing and implementing dataflow-based signal processing applications on multi-core architectures. HMBE provides complementary capabilities to HTGS (Hybrid Task Graph Scheduler), a recently-introduced software tool for implementing scalable workflows for high performance computing applications on compute nodes with high core counts and multiple GPUs. HMBE integrates model-based design approaches, founded on dataflow principles, with advanced design optimization techniques provided in HTGS. This integration contributes to (a) making the application of HTGS more systematic and less time consuming, (b) incorporating additional dataflow-based optimization capabilities with HTGS optimizations, and (c) automating significant parts of the HTGS-based design process using a principled approach. In this paper, we present HMBE with an emphasis on the model-based design approaches and the novel dynamic scheduling techniques that are developed as part of the tool. We demonstrate the utility of HMBE via two case studies: an image stitching application for large microscopy images and a background subtraction application for multispectral video streams.",
keywords = "Dataflow, Memory management, Multicore platforms, Scheduling",
author = "Jiahao Wu and Timothy Blattner and Walid Keyrouz and Bhattacharyya, {Shuvra S.}",
year = "2018",
doi = "10.1007/s11265-018-1412-5",
language = "English",
pages = "1--14",
journal = "Journal of Signal Processing Systems",
issn = "1939-8018",
publisher = "Springer Verlag",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Model-Based Dynamic Scheduling for Multicore Signal Processing

AU - Wu, Jiahao

AU - Blattner, Timothy

AU - Keyrouz, Walid

AU - Bhattacharyya, Shuvra S.

PY - 2018

Y1 - 2018

N2 - This paper presents a model-based design method and a corresponding new software tool, the HTGS Model-Based Engine (HMBE), for designing and implementing dataflow-based signal processing applications on multi-core architectures. HMBE provides complementary capabilities to HTGS (Hybrid Task Graph Scheduler), a recently-introduced software tool for implementing scalable workflows for high performance computing applications on compute nodes with high core counts and multiple GPUs. HMBE integrates model-based design approaches, founded on dataflow principles, with advanced design optimization techniques provided in HTGS. This integration contributes to (a) making the application of HTGS more systematic and less time consuming, (b) incorporating additional dataflow-based optimization capabilities with HTGS optimizations, and (c) automating significant parts of the HTGS-based design process using a principled approach. In this paper, we present HMBE with an emphasis on the model-based design approaches and the novel dynamic scheduling techniques that are developed as part of the tool. We demonstrate the utility of HMBE via two case studies: an image stitching application for large microscopy images and a background subtraction application for multispectral video streams.

AB - This paper presents a model-based design method and a corresponding new software tool, the HTGS Model-Based Engine (HMBE), for designing and implementing dataflow-based signal processing applications on multi-core architectures. HMBE provides complementary capabilities to HTGS (Hybrid Task Graph Scheduler), a recently-introduced software tool for implementing scalable workflows for high performance computing applications on compute nodes with high core counts and multiple GPUs. HMBE integrates model-based design approaches, founded on dataflow principles, with advanced design optimization techniques provided in HTGS. This integration contributes to (a) making the application of HTGS more systematic and less time consuming, (b) incorporating additional dataflow-based optimization capabilities with HTGS optimizations, and (c) automating significant parts of the HTGS-based design process using a principled approach. In this paper, we present HMBE with an emphasis on the model-based design approaches and the novel dynamic scheduling techniques that are developed as part of the tool. We demonstrate the utility of HMBE via two case studies: an image stitching application for large microscopy images and a background subtraction application for multispectral video streams.

KW - Dataflow

KW - Memory management

KW - Multicore platforms

KW - Scheduling

U2 - 10.1007/s11265-018-1412-5

DO - 10.1007/s11265-018-1412-5

M3 - Article

SP - 1

EP - 14

JO - Journal of Signal Processing Systems

JF - Journal of Signal Processing Systems

SN - 1939-8018

ER -