Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Model-based Dynamic Scheduling for Multicore Implementation of Image Processing Systems

Published

Author(s)

Timothy J. Blattner, Walid Keyrouz, Jiahao Wu, Shuvra S. Bhattacharyya

Abstract

In this paper, we present a new software tool, called HTGS Model-based Engine (HMBE), for the design and implementation of multicore signal processing applications. HMBE provides complementary capabilities to HTGS (Hybrid Task Graph Scheduler), which is a recently-introduced software tool for implementing scalable workflows for high performance computing applications. HMBE integrates advanced design optimization techniques provided in HTGS with model-based approaches that are founded on dataflow principles. Such 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. In this paper, we present HMBE with an emphasis on novel dynamic scheduling techniques that are developed as part of the tool. We demonstrate the utility of HMBE through a case study involving an image stitching application for large scale microscopy images.
Proceedings Title
IEEE Global Conference on Signal and Information Processing
Conference Dates
November 14-16, 2017
Conference Location
Honolulu, HI

Keywords

dataflow

Citation

Blattner, T. , Keyrouz, W. , Wu, J. and Bhattacharyya, S. (2017), Model-based Dynamic Scheduling for Multicore Implementation of Image Processing Systems, IEEE Global Conference on Signal and Information Processing, Honolulu, HI (Accessed April 13, 2024)
Created October 5, 2017, Updated September 17, 2018