Summary Workload‐aware loop schedulers were introduced to deliver better performance than classical loop scheduling strategies. However, they presented limitations such as inflexible built‐in workload estimators and suboptimal chunk scheduling. Targeting these challenges, we proposed previously a workload‐aware scheduling strategy called BinLPT, which relies on three features: (i) user‐supplied estimations of the workload of the loop; (ii) a greedy heuristic that adaptively partitions the iteration space in several chunks; and (iii) a scheduling scheme based on the Longest Processing Time (LPT) rule and on‐demand technique. In this paper, we present two new contributions to the state‐of‐the‐art. First, we introduce a multiloop support feature to BinLPT, which enables the reuse of estimations across loops. Based on this feature, we integrated BinLPT into a real‐world elastodynamics application, and we evaluated it running on a supercomputer. Second, we present an evaluation of BinLPT using simulations as well as synthetic and application kernels. We carried out this analysis on a large‐scale NUMA machine under a variety of workloads. Our results revealed that BinLPT is better at balancing the workloads of the loop iterations and this behavior improves as the algorithmic complexity of the loop increases. Overall, BinLPT delivers up to 37.15% and 9.11% better performance than well‐known loop scheduling strategies, for the application kernels and the elastodynamics simulation, respectively.

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Distributed real-time threads are schedulable entities with an end-to-end deadline that traverse nodes, carrying their scheduling context. In each node, the thread will be locally scheduled and predictions about deadline missing allow that actions are carried out to improve system performance. This paper presents a task model and deadline partitioning algorithms that consider the possibility of a distributed thread to follow different paths. The future execution flow of the distributed thread is only probabilistic known before its execution. End-to-end deadline missing prediction mechanisms can be carried out through definition of estimated local deadlines. Simulations show that the proposed prediction mechanism presents good results in overloaded systems.

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