首页|Exploit the data level parallelism and schedule dependent tasks on the multi-core processors
Exploit the data level parallelism and schedule dependent tasks on the multi-core processors
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NSTL
Elsevier
With the growing use of multi-core processors in the market, efficient and effective task parallelization strategies are on huge demand, so are the task scheduling algorithms. The scalability and efficiency of the existing algorithms on multi-core task scheduling need to be improved. To schedule real-time tasks on a multi-core processor, any pair of inter-dependent tasks must be executed following their original execution order. The directed acyclic graph (DAG) is commonly used to study the internal structure of a program. In this work, we investigated the property of the data dependency to eliminate the unnecessary execution constraints, and improved the DAG model by incorporating the temporal prop-erty of these dependencies. Based on such a model, we proposed a dynamic decomposed scheduling (DDS) strategy. With DDS, the dependent tasks could be released and executed earlier before the completion of their precedent tasks without producing any data hazards. The experiments were conducted on both synthesized tasks and real industrial embedded applications, the results show that DDS has a good performance in multi-core task schedul-ing, and it outperforms the state-of-the-art scheduling algorithms including the decom-posed scheduling, the global scheduling, and the federated scheduling.(c) 2021 Elsevier Inc. All rights reserved.
NSTL
Elsevier
