Real-time Scheduling

Reserch on Real-time Scheduling

Multi-core Scheduling

Targeting global EDF scheduling, this paper proposes a conditionally optimal algorithm for parallelizing tasks with parallelization freedom. For this, we extend the interferencebased sufficient schedulability analysis and derive monotonic increasing properties of both tolerance and interference for the schedulability. Leveraging those properties, we propose a oneway search based conditionally optimal algorithm with polynomial time complexity. Our extensive experiments through both simulation and actual implementation show that our proposed approach can significantly improve the schedulability up to 60 percent.

Recent parallel programming frameworks such as OpenCL and OpenMP allow us to enjoy the parallelization freedom for real-time tasks. The parallelization freedom creates the time vs. density tradeoff problem in fluid scheduling, i.e., more parallelization reduces thread execution times but increases the density. By system-widely exercising this tradeoff, we propose optimal parameter tuning of real-time tasks aiming at maximizing the schedulability of multicore fluid scheduling. Our experimental study by both simulation and actual implementation shows that the proposed approach well balances the time and the density, and results in up to 80% improvement of the schedulability.

Past researches on multicore scheduling assume that a computational unit has already been parallelized into a prefixed number of threads. However, with recent technologies such as OpenCL, a computational unit can be parallelized in many different ways with runtime selectable numbers of threads.This paper proposes an optimal algorithm for parallelizing and scheduling a set of parallel tasks with multiple parallelization options on multiple CPU cores. The proposed algorithm is validated through both simulation and actual implementation. To the best of our knowledge, this is the first work addressing the problem of scheduling real-time tasks with multiple parallelization options on multiple CPU cores.



Direct Acyclic Graph Tasks Schedule

In this paper, we discuss a method for scheduling a set of parallel tasks modeled by a generic DAG task model. We present a method that handles the intra-task parallelism between computational units as well as the parallelization of individual computational units in order to schedule the tasks on multiple CPU cores. We introduce a density packing problem that describes our approach for handling intra-task parallelism in order to minimize the overall task minimum peak density.

GPGPU Scheduling

ETC

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