Partitioned scheduling of parallel real-time tasks on multiprocessor systems
ACM SIGBED Review - Work-in-Progress (WiP) Session of the 23rd Euromicro Conference on Real-Time Systems (ECRTS 2011)
Server-based scheduling of parallel real-time tasks
Proceedings of the tenth ACM international conference on Embedded software
A semi-partitioned approach for parallel real-time scheduling
Proceedings of the 20th International Conference on Real-Time and Network Systems
Journal of Parallel and Distributed Computing
Dynamic scheduling algorithm for parallel real-time graph tasks
ACM SIGBED Review - Special Issue on the 24th Euromicro Conference on Real-Time Systems
Exploiting just-enough parallelism when mapping streaming applications in hard real-time systems
Proceedings of the 50th Annual Design Automation Conference
Parallel scheduling for cyber-physical systems: analysis and case study on a self-driving car
Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems
Global EDF scheduling of directed acyclic graphs on multiprocessor systems
Proceedings of the 21st International conference on Real-Time Networks and Systems
Real-time programming on accelerator many-core processors
Proceedings of the 2013 ACM SIGAda annual conference on High integrity language technology
DFTS: A dynamic fault-tolerant scheduling for real-time tasks in multicore processors
Microprocessors & Microsystems
Supporting soft real-time parallel applications on multiprocessors
Journal of Systems Architecture: the EUROMICRO Journal
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Massively multi-core processors are rapidly gaining market share with major chip vendors offering an ever increasing number of cores per processor. From a programming perspective, the sequential programming model does not scale very well for such multi-core systems. Parallel programming models such as OpenMP present promising solutions for more effectively using multiple processor cores. In this paper, we study the problem of scheduling periodic real-time tasks on multiprocessors under the fork join structure used in OpenMP. We illustrate the theoretical best-case and worst-case periodic fork-join task sets from a processor utilization perspective. Based on our observations of these task sets, we provide a partitioned preemptive fixed-priority scheduling algorithm for periodic fork-join tasks. The proposed multiprocessor scheduling algorithm is shown to have a resource augmentation bound of 3.42, which implies that any task set that is feasible on m unit speed processors can be scheduled by the proposed algorithm on m processors that are 3:42 times faster.