Robust non-preemptive hard real-time scheduling for clustered multicore platforms

Lombardi,; Milano,; Benini,

doi:10.1109/date.2009.5090773

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…First, it reorders the asynchronous kernel jobs in the Job Queue by keeping a partial order in the original VP. It is a non-preemptive, optimal scheduler augmented for job dependencies [14]. Second, it combines identical kernel requests in the Job Queue into one single kernel job, by using Kernel Coalescing, also discussed in Section 3.…”

Section: Gpu Multiplexing For Simulationmentioning

confidence: 99%

Σvp

Jung

Carloni

2015

Proceedings of the 52nd Annual Design Automation Conference

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Despite their proliferation across many embedded platforms, GPUs present still many challenges to embedded-system designers. In particular, GPU-optimized software actually slows down the execution of embedded applications on system simulators. This problem is worse for concurrent simulations of multiple instances of embedded devices equipped with GPUs. To address this challenge, we present ΣVP, a framework to accelerate concurrent simulations of multiple virtual platforms by leveraging the physical GPUs present on the host machine. ΣVP multiplexes the host GPUs to speed up the concurrent simulations without requiring any change to the original GPU-optimized application code. With ΣVP, GPU applications run more than 600 times faster than GPU-software emulation on virtual platforms. We also propose Kernel Interleaving and Kernel Coalescing, two techniques that further speed up the simulation by one order of magnitude. Finally, we show how ΣVP supports simulation-based functional validation and performance/power estimation.

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Section: Gpu Multiplexing For Simulationmentioning

confidence: 99%

Σvp

Jung

Carloni

2015

Proceedings of the 52nd Annual Design Automation Conference

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“…The adopted temporal model consists of a constraint based formulation of the STNU formalism 6 ; we rely on 6 With the restriction that the time bounds must be all non-negative. constraint propagation to enforce consistency.…”

Section: A the Time Modelmentioning

confidence: 99%

“…Building over our previous work [6], [7], we leverage a hybrid off-line/on-line technique from the Project Scheduling domain, known as Precedence Constraint Posting (PCP) [8]. A schedule in PCP consists of a set of additional arcs in the task graph: this is implemented at run-time by delaying each task until all its predecessors (either natural or artificial) are over.…”

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confidence: 99%

Robust Scheduling of Task Graphs under Execution Time Uncertainty

Lombardi

Milano

Benini

2013

IEEE Trans. Comput.

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Abstract-Effective multicore computing requires to make efficient usage of the computational resources on a chip. Offline mapping and scheduling can be applied to improve the performance, but classical approaches require considerable a-priori knowledge of the target application. In a practical setting, precise information is often unavailable; one can then resort to approximate time and resource usage figures, but this usually requires to make conservative assumptions. The issue is further stressed if real-time guarantees must be provided. We tackle predictable and efficient non-preemptive scheduling of multi-task applications in the presence of duration uncertainty. Hard real-time guarantees are provided with limited idle time insertion, by exploiting a hybrid off-line/on-line technique known as Precedence Constraint Posting (PCP). Our approach does not require probability distributions to be specified, relying instead on simple and cheaper-toobtain information (bounds, average values). The method has been tested on synthetic applications/platforms and compared with an off-line optimized Fixed Priority Scheduling (FPS) approach and a pure on-line FIFO scheduler; the results are very promising, as the PCP schedules exhibit good stability and improved average execution time (14% on average, up to 30% versus FPS and up to 40% versus the FIFO scheduler).

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“…Another approach to improve resource utilization is based on specifying lower and upper bounds on the execution time. With the help of precedence constraints, a robust schedule can also be constructed, which avoids possible scheduling anomalies [27].…”

Section: Case Studies and Current Researchmentioning

confidence: 99%

How to Safely Integrate Multiple Applications on Embedded Many-Core Systems by Applying the “Correctness by Construction” Principle

Hilbrich¹

2012

Advances in Software Engineering

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Software-intensive embedded systems, especially cyber-physical systems, benefit from the additional performance and the small power envelope offered by many-core processors. Nevertheless, the adoption of a massively parallel processor architecture in the embedded domain is still challenging. The integration of multiple and potentially parallel functions on a chip—instead of just a single function—makes best use of the resources offered. However, this multifunction approach leads to new technical and nontechnical challenges during the integration. This is especially the case for a distributed system architecture, which is subject to specific safety considerations. In this paper, it is argued that these challenges cannot be effectively addressed with traditional engineering approaches. Instead, the application of the “correctness by construction” principle is proposed to improve the integration process.

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Robust non-preemptive hard real-time scheduling for clustered multicore platforms

Cited by 9 publications

References 14 publications

Σvp

Σvp

Robust Scheduling of Task Graphs under Execution Time Uncertainty

How to Safely Integrate Multiple Applications on Embedded Many-Core Systems by Applying the “Correctness by Construction” Principle

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