Abstract
Four scheduling strategies for dataflow graphs onto parallel processors are classified: (1) fully dynamic, (2) static-assignment, (3) self-timed, and (4) fully static. Scheduling techniques valid for strategies (2), (3), and (4) are proposed. The focus is on dataflow graphs representing data-dependent iteration. A known probability mass function for the number of cycles in the data-dependent iteration is assumed, and how a compile-time decision about assignment and/or ordering as well as timing can be made is shown. The criterion used is to minimize the expected total idle time caused by the iteration. In certain cases, this will also minimize the expected makespan of the schedule. How to determine the number of processors that should be assigned to the data-dependent iteration is shown. The method is illustrated with a practical programming example.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
