A Capacity Augmentation Bound for Real-Time Constrained-Deadline Parallel Tasks Under GEDF

Sun, Jun; Guan, Nan; Jiang, Xu; Chang, Shuangshuang; Guo, Zhishan; Deng, Qingxu; Yi, Wang

doi:10.1109/tcad.2018.2857362

Cited by 15 publications

(2 citation statements)

References 25 publications

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“…Parallel hard real-time DAG tasks may be scheduled by federated [10,26,36,50,59] or global [11,24,46,47] policies. Federated scheduling improves the analytical bounds of global scheduling by dedicating cores to tasks that require more than one core to meet their deadlines.…”

Section: Related Workmentioning

confidence: 99%

“…Execution assignment begins by creating a set number of executable objects o per task. Each object is given a single thread WCET c 1 , a growth factor of F. The single thread execution value of each object is assigned a random value from the range c 1 ∈ [1,50]. The growth factor of each object is assigned a random value from the range [0.2, F] Every node of the task is assigned exactly one executable object and one thread of execution.…”

Section: Appendixmentioning

confidence: 99%

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Bringing Inter-Thread Cache Benefits to Federated Scheduling -- Extended Results & Technical Report

Tessler,

Modekurthy,

Fisher

et al. 2020

Preprint

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Multiprocessor scheduling of hard real-time tasks modeled by directed acyclic graphs (DAGs) exploits the inherent parallelism presented by the model. For DAG tasks, a node represents a request to execute an object on one of the available processors. In one DAG task, there may be multiple execution requests for one object, each represented by a distinct node. These distinct execution requests offer an opportunity to reduce their combined cache overhead through coordinated scheduling of objects as threads within a parallel task. The goal of this work is to realize this opportunity by incorporating the cacheaware BUNDLE-scheduling algorithm into federated scheduling of sporadic DAG task sets. This is the first work to incorporate instruction cache sharing into federated scheduling. The result is a modification of the DAG model named the DAG with objects and threads (DAG-OT). Under the DAG-OT model, descriptions of nodes explicitly include their underlying executable object and number of threads. When possible, nodes assigned the same executable object are collapsed into a single node; joining their threads when BUNDLEscheduled. Compared to the DAG model, the DAG-OT model with cache-aware scheduling reduces the number of cores allocated to individual tasks by approximately 20 percent in the synthetic evaluation and up to 50 percent on a novel parallel computing platform implementation. By reducing the number of allocated cores, the DAG-OT model is able to schedule a subset of previously infeasible task sets.

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