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Model-Based Dynamic Scheduling for Multicore Signal Processing

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalJournal of Signal Processing Systems
Early online date2018
Publication statusPublished - 2018
Publication typeA1 Journal article-refereed


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.


  • Dataflow, Memory management, Multicore platforms, Scheduling

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Field of science, Statistics Finland