Sirap

Behnam, Moris; Shin, Insik; Nolte, Thomas; Nolin, Mikael

doi:10.1145/1289927.1289970

Cited by 87 publications

(3 citation statements)

References 26 publications

(40 reference statements)

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“…For instance, Lamastra et al 34 and De Niz et al 35 proposed budget inheritance mechanisms (also called proxy execution ) for uniprocessor systems, which allow the reservation managing a task blocked on a shared resource to inherit the budget of another reservation that is waiting for the same resource. This mechanism has been later extended to multiprocessor systems, for example, by Faggioli et al 36 Differently, the SIRAP 37 protocol performs a budget check every time a lock on a shared resource is requested: if the budget is enough to complete the critical section the lock is given to the requesting reservation, otherwise the access is granted only at the next budget replenishment. The BROE 38,39 protocol performs a similar budget check at each lock request, but, if the current budget is not enough to complete the execution of the critical section, the budget is replenished to the maximum value and the deadline is postponed proportionally.…”

Section: Related Workmentioning

confidence: 99%

Timing isolation and improved scheduling of deep neural networks for real‐time systems

2020

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In recent years, the performance of deep neural networks (DNNs) is significantly improved, making them suitable for many application fields, such as autonomous driving, advanced robotics, and industrial control. Despite a lot of research being devoted to improving the accuracy of DNNs, only limited efforts have been spent to enhance their timing predictability, required in several real-time applications. This paper proposes a software infrastructure based on the Linux operating system to integrate DNNs within a real-time multicore system. It has been realized by modifying both the internal scheduler of the popular TensorFlow framework and the SCHED_DEADLINE scheduling class of Linux. The proposed infrastructure allows providing timing isolation of DNN inference tasks, hence improving the determinism of the temporal interference generated by TensorFlow. The proposal is finally evaluated with a case study derived from a state-of-the-art benchmark inspired by an autonomous industrial system. Extensive experiments demonstrate the effectiveness of the proposed solution and show a significant reduction of both average and longest-observed response times of TensorFlow tasks.

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Section: Related Workmentioning

confidence: 99%

Timing isolation and improved scheduling of deep neural networks for real‐time systems

2020

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“…HSRP uses budget overrun and payback mechanisms to limit priority inversion. SIRAP (Subsystem Integration and Resource Allocation Policy) [98] uses the idea of self-blocking to bound delays on accessing shared resources without knowing the timing parameters of other subsystems. RRP (Rollback Resource Policy) [99] uses a rollback mechanism to avoid a lock-holding task to be blocked while holding a lock.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, multi-core synchronization mechanisms designed for non-hierarchical scheduling, such as MPCP [12,13] and MSRP [87], can lead to excessive blocking times due to the preemption and budget depletion of VCPUs, as discussed in Section 2.3.1. Available solutions in the uni-core hierarchical scheduling context [97,98,99] have not yet been extended to multi-core platforms. More importantly, in current virtualization solutions, the hypervisor is unaware of the executions of critical sections of tasks within VCPUs and there is no systematic mechanism to do so.…”

Section: Synchronization For Multi-core Virtual Machinesmentioning

confidence: 99%

Towards Predictable Real-Time Performance on Multi-Core Platforms

Kim¹

2016

Preprint

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Cyber-physical systems (CPS) integrate sensing, computing, communication and actuation capabilities to monitor and control operations in the physical environment. A key requirement of such systems is the need to provide predictable real-time performance: the timing correctness of the system should be analyzable at design time with a quantitative metric and guaranteed at runtime with high assurance. This requirement of predictability is particularly important for safety-critical domains such as automobiles, aerospace, defense, manufacturing and medical devices.The work in this dissertation focuses on the challenges arising from the use of modern multi-core platforms in CPS. Even as of today, multi-core platforms are rarely used in safety-critical applications primarily due to the temporal interference caused by contention on various resources shared among processor cores, such as caches, memory buses, and I/O devices. Such interference is hard to predict and can significantly increase task execution time, e.g., up to 12× on commodity quad-core platforms. To address the problem of ensuring timing predictability on multi-core platforms, we develop novel analytical and systems techniques in this dissertation. Our proposed techniques theoretically bound temporal interference that tasks may suffer from when accessing shared resources. Our techniques also involve software primitives and algorithms for real-time operating systems and hypervisors, which significantly reduce the degree of the temporal interference. Specifically, we tackle the issues of cache and memory contention, locking and synchronization, interrupt handling, and access control for computational accelerators such as general-purpose graphics processing units (GPGPUs), all of which are crucial to achieving predictable real-time performance on a modern multi-core platform. Our solutions are readily applicable to commodity multi-core platforms, and can be used not only for developing new systems but also migrating existing applications from single-core to multi-core platforms. vi This dissertation would have been impossible without the help and support of many people. First and foremost, I would like to thank my advisor, Prof. Raj Rajkumar. I was lucky to work with Raj. His guidance and expertise have made me a better thinker, writer, and researcher. Raj gave me opportunities to participate in exciting projects, demonstrate my research results, and mentor other students, all of which led me to become an independent researcher and to pursue an academic career. I am grateful to the members of my thesis committee, Prof. Onur Mutlu, Prof. Anthony Rowe, and Dr. Shige Wang for their time, effort and inputs in completing this dissertation. Thanks to Onur for his insights on various aspects of my work. I learned a lot from Onur on computer architectures, which was a great asset for my research. Thanks to Anthony for his feedback and advice, even since my very early days at CMU. I enjoyed lively conversations with Anthony and liked to hear his view on cyber-physical...

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Improving responsiveness of time‐sensitive applications by exploiting dynamic task dependencies

et al. 2017

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In this paper, a mechanism is presented for reducing priority inversion in multiprogrammed computing systems. Contrary to well-known approaches from the literature, this paper tackles cases where the dependency relationships among tasks cannot be known in advance to the operating system. The presented mechanism allows tasks to explicitly declare aforementioned relationships, enabling the operating system scheduler to take advantage of such information and trigger priority inheritance, resulting in reduced priority inversion.We present the prototype implementation of the concept within the Linux kernel in the form of modifications to the standard Portable Operating System Interface (POSIX) condition variable code, along with an extensive evaluation, including a quantitative assessment of the benefits for applications making use of the technique and comprehensive overhead measurements. In addition, we present an associated technique for the theoretical schedulability analysis of a system using the new mechanism, which is useful to determine whether all tasks can meet their deadlines or not, in the specific scenario of tasks interacting only through remote procedure calls and under partitioned scheduling.

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Sirap

Cited by 87 publications

References 26 publications

Timing isolation and improved scheduling of deep neural networks for real‐time systems

Timing isolation and improved scheduling of deep neural networks for real‐time systems

Towards Predictable Real-Time Performance on Multi-Core Platforms

Improving responsiveness of time‐sensitive applications by exploiting dynamic task dependencies

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