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Strict Partitioning for Sporadic Rigid Gang Tasks

arXiv 2403.10726 ↗First posted 15 Mar 2024cs.DC cs.AI cs.ARcited by 0

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Abstract (arXiv)

The rigid gang task model is based on the idea of executing multiple threads simultaneously on a fixed number of processors to increase efficiency and performance. Although there is extensive literature on global rigid gang scheduling, partitioned approaches have several practical advantages (e.g., task isolation and reduced scheduling overheads). In this paper, we propose a new partitioned scheduling strategy for rigid gang tasks, named strict partitioning. The method creates disjoint partitions of tasks and processors to avoid inter-partition interference. Moreover, it tries to assign tasks with similar volumes (i.e., parallelisms) to the same partition so that the intra-partition interference can be reduced. Within each partition, the tasks can be scheduled using any type of scheduler, which allows the use of a less pessimistic schedulability test. Extensive synthetic experiments and a case study based on Edge TPU benchmarks show that strict partitioning achieves better schedulability performance than state-of-the-art global gang schedulability analyses for both preemptive and non-preemptive rigid gang task sets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 80 references that are in the map. First posted on arXiv 15 Mar 2024.

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References · 7

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  3. 2014Going Deeper with Convolutionscited by 4,231
  4. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  5. 2021An Evaluation of Edge TPU Accelerators for Convolutional Neural Networkscited by 39
  6. 2017Semi-Federated Scheduling of Parallel Real-Time Tasks on Multiprocessorscited by 2
  7. 2017Reservation-Based Federated Scheduling for Parallel Real-Time Taskscited by 2

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