A Safari Through the MPSoC Run-Time Management Jungle

Abstract

The multiprocessor SoC (MPSoC) revolution is fueled by the need to execute multiple advanced multimedia applications on a single embedded computing platform. At design-time, the applications that will run in parallel and their respective user requirements are unknown. Hence, a run-time manager (RTM) is needed to match all application needs with the available platform resources and services. Creating such a runtime manager requires two decisions. First, one needs to decide what functionality to implement. Second, one has to decide how to implement this functionality in order to meet boundary conditions like e.g. real-time performance. This paper is the first to detail a generic view on MPSoC run-time management functionality and its design space trade-offs. We substantiate the run-time components and the implementation tradeoffs with academic state-of-the-art solutions and a brief overview of some industrial multiprocessor run-time management examples. We show a clear trend towards more hardware acceleration, a limited distribution of management functionality over the platform and increasing support for adaptive multimedia applications.In addition, we briefly detail upcoming run-time management research issues.

Hard-real-time scheduling of data-dependent tasks in embedded streaming applications

“…Any start time later than that guarantees also strictly periodic execution since vj will always find enough data to execute in a strictly periodic way. Equation 22 can be re-written as:…”

“…Unfortunately, no such de-facto solution exists yet for the second challenge of processor allocation [22]. For a long time, self-timed scheduling was considered the most appropriate policy for streaming applications modeled as dataflow graphs [13,26].…”

“…at system design-time is forcing a shift towards run-time scheduling approaches as explained in [12]. Most of the existing run-time scheduling solutions assume applications modeled as task graphs and provide best-effort or soft-real-time services [22]. Few run-time scheduling solutions exist which support applications modeled using a MoC and provide hard-real-time services [10,3,19,20].…”

On the hard-real-time scheduling of embedded streaming applications

“…Unfortunately, no such de-facto solution exists yet for the second challenge of processor allocation [23]. For a long time, self-timed scheduling was considered the most appropriate policy for streaming applications modeled as dataflow graphs [14,28].…”

“…at system design-time is forcing a shift towards run-time scheduling approaches as explained in [13]. Most of the existing run-time scheduling solutions assume applications modeled as task graphs and provide best-effort or soft-real-time quality-of-service (QoS) [23]. Few run-time scheduling solutions exist which support applications modeled using a MoC and provide hard-real-time QoS [4,11,20,21].…”