Language and Compilation of Parallel Programs for *-Predictable MPSoC Execution Using Invasive Computing

TLDR: It is shown that statically proven quality guarantees may be enforced on many multi-core architectures by a presented hybrid mapping approach and a real-world case study from the domain of heterogeneous robot vision is given to demonstrate the capabilities of this approach to guarantee statically analyzed best and worst-case timing requirements on latency and throughput.

Abstract: The predictability of execution qualities including timeliness, power consumption, and fault-tolerability is of utmost importance for the successful introduction of multi-core architectures in embedded systems requiring guarantees rather than best effort behavior. Examples are real-time and/or safety-critical parallel applications. In particular for future many-core architectures, analysis tools for proving such properties to hold for a given application irrespective of other workload either suffer from computational complexity. Or, sound bounds are of no practical interest due to severe interferences of resources and software at multiple levels. In view of abundant computational and memory resources becoming available, we propose to apply the principles of invasive computing to avoid sharing of resources at run time as much as possible. We subsequently show that statically proven quality guarantees may be enforced on many multi-core architectures by a presented hybrid mapping approach. Rather than fixed resource mappings, this approach provides only constellations of resource allocations to the run-time system that searches for such constellations and assigns the invader a suitable claim of resources, if possible. We have implemented this hybrid approach and the interface to the language InvadeX10, a library-based extension of the X10 programming language. In this extension, so-called requirements on execution qualities such as deadlines (e.g., in the form of latency constraints) may be annotated to individual programs or even program segments. These are then translated into satisfying resource constellations that need to be found at run time prior to admitting a parallel application to start, respectively continue in view of required execution quality requirements. We give a real-world case study from the domain of heterogeneous robot vision to demonstrate the capabilities of this approach to guarantee statically analyzed best and worst-case timing requirements on latency and throughput.