Online resource management in a multiprocessor with a network-on-chip
Proceedings of the 2007 ACM symposium on Applied computing
Multiprocessor resource allocation for throughput-constrained synchronous dataflow graphs
Proceedings of the 44th annual Design Automation Conference
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Throughput Constraint for Synchronous Data Flow Graphs
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
An open computing resource management framework for real-time computing
HiPC'08 Proceedings of the 15th international conference on High performance computing
Proceedings of the Conference on Design, Automation and Test in Europe
Dynamic and adaptive allocation of applications on MPSoC platforms
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Hard-real-time scheduling of data-dependent tasks in embedded streaming applications
EMSOFT '11 Proceedings of the ninth ACM international conference on Embedded software
Model checking of scenario-aware dataflow with CADP
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
Maximum-throughput mapping of SDFGs on multi-core SoC platforms
Journal of Parallel and Distributed Computing
Flexible filters in stream programs
ACM Transactions on Embedded Computing Systems (TECS)
Journal of Systems Architecture: the EUROMICRO Journal
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An embedded multiprocessor that can run multiple hard-real-time (HRT) jobs simultaneously has to guarantee that enough resources are available to meet the timing constraints.It is essential that both application model and hardware are tailored to this goal.Moreover, suitable resource allocation and scheduling are needed. This paper proposes a resource allocator that gives guarantees for HRT streaming applications.Because new jobs arrive during operation, resource allocation is performed at run-time.This provides admission control.Resource budget enforcement is handled by local schedulers.We formalize our resource allocation problem and show that it is NP-complete.We developed heuristics to tackle the problem during run-time and evaluated them.A modified First-fit Vector Bin-Packing algorithm provides a good solution; it can allocate 95% of the resources, while handling a large number of job arrivals and departures on a heavily loaded system.