Intra-Task Priority Assignment in Real-Time Scheduling of DAG Tasks on Multi-Cores

He, Qingqiang; Jiang, Xu; Guan, Nan; Guo, Zhishan

doi:10.1109/tpds.2019.2910525

Cited by 53 publications

(68 citation statements)

References 24 publications

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“…The GoSu model consistently shows competitive performance compared to several state-of-the-art DAG scheduling heuristic methods based on fixed-priority assignments (i.e., [1], [2]) under various experimental settings. Furthermore, the model often achieves better performance than those heuristic methods particularly in nontrivial task configurations, e.g., showing a 2∼3% gain in the slowdown of achieved makespans for the cases when the number of processors is 3 or 4 with moderate parallelism and when the number of processors is between 3 and 8 with high parallelism (Figure 4(b) and (c)).…”

Section: Introductionmentioning

confidence: 91%

“…In the real-time system literature, fixed-priority scheduling schemes for parallel tasks have been receiving attention relatively recently in [1], [2], while many works on DAG task scheduling considered dynamic scheduling cases [3], [5], [6], [12], [13]. By fixed-priority scheduling, subtasks (or nodes) in a DAG task can be scheduled globally on all processors, and furthermore, a platform can be free from overhead issues of online scheduling in real-time systems [1], [14].…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, as non-preemptive task models can avoid the overhead issue of migration and switching tasks, recently, priority-based non-preemptive scheduling for a DAG task has gained much attention [1]. The problem has been tackled by investigating scheduler techniques with priority assignments, which take a set of subtasks in a single DAG as input and produce a priority order for the subtasks and their non-deterministic execution order [1], [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Global DAG Task Scheduler Using Deep Reinforcement Learning and Graph Convolution Network

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Section: Introductionmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Global DAG Task Scheduler Using Deep Reinforcement Learning and Graph Convolution Network

et al. 2021

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“…They use nested fork-join structured DAGs to propose both accurate and efficient solution. In addition, He et al [14] study the global scheduling of multiple DAG tasks on multi-core processors. They also define sub-tasks execution order inside the same graph to reduce the response time.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Schedulability Analysis for Precedence Constrained Tasks on Partitioned Multi-core

Ben-Amor

Cucu-Grosjean

Mezouak

et al. 2020

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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The design of cyber-physical systems (CPSs) is facing the explosion of new functionalities requiring increased computation capacities and, thus, the introduction of multicore processors. Moreover, some functionalities may impose precedence constraints between the programs implementing these new functionalities. While important effort has been dedicated to the scheduling of precedence constraints tasks on multi-core processors, existing work considers either partitioned scheduling for a single precedence graph defining precedence constraints between tasks, or global scheduling policies.In this paper, we consider partitioned scheduling for multiple precedence graphs defining precedence constraints between tasks. The variability of execution times and of communication times is described by probability distributions. We propose a new response time analysis over-performing existing ILP-based results. Thanks to its scalability, our solution is extendable to a probabilistic version and we validate it on a PX4 drone autopilot. Beside this autopilot for our experiments, we implemented a probabilistic extension of a multi-core processor simulator, SimSo. A priority assignment heuristic allowing parallel executions is also proposed. Thanks to its adaptation to partitioned scheduling, our heuristic has better performances than existing solutions and its performances are, also, compared against a genetic-based heuristic.

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“…As previous work points out, new high-level programming language models for realtime systems are therefore needed [13]. Directed Acyclic Graphs (DAGs) are a very general application model that can capture any possible interaction among threads [14]. In contrast, we propose to constrain the application structure to a set of composable skeletons to improve programmability, resource usage and timing analysis at the price of often expendable generality w.r.t.…”

Section: Introductionmentioning

confidence: 99%

Enforcing Deadlines for Skeleton-based Parallel Programming

Metzger

Cole

Fensch

et al. 2020

2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)

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High throughput applications with real-time guarantees are increasingly relevant. For these applications, parallelism must be exposed to meet deadlines. Directed Acyclic Graphs (DAGs) are a popular and very general application model that can capture any possible interaction among threads. However, we argue that by constraining the application structure to a set of composable "skeletons", at the price of losing some generality w.r.t. DAGs, the following advantages are gained: (i) a finer model of the application enables tighter analysis, (ii) specialised scheduling policies are applicable, (iii) programming is simplified, (iv) specialised implementation techniques can be exploited transparently, and (v) the program can be automatically tuned to minimise resource usage while still meeting its hard deadlines. As a first step towards a set of real-time skeletons we conduct a case study with the job farm skeleton and the hard realtime XMOS xCore-200 microcontroller. We present an analytical framework for job farms that reduces the number of required cores by scheduling jobs in batches, while ensuring that deadlines are still met. Our experimental results demonstrate that batching reduces the minimum sustainable period by up to 22%, leading to a reduced number of required cores. The framework chooses the best parameters in 83% of cases and never selects parameters that cause deadline misses. Finally, we show that the overheads introduced by the skeleton abstraction layer are negligible. 4 We remark that this skeleton is called "task farm" in the parallel computing community. However, to not overload the term "task" with conflicting interpretations, we use the term job farm, which, we believe, better represents our intended interpretation in real-time systems.

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Intra-Task Priority Assignment in Real-Time Scheduling of DAG Tasks on Multi-Cores

Cited by 53 publications

References 24 publications

A Global DAG Task Scheduler Using Deep Reinforcement Learning and Graph Convolution Network

A Global DAG Task Scheduler Using Deep Reinforcement Learning and Graph Convolution Network

Probabilistic Schedulability Analysis for Precedence Constrained Tasks on Partitioned Multi-core

Enforcing Deadlines for Skeleton-based Parallel Programming

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