Race-Condition-Aware and Hardware-Oriented Task Partitioning and Scheduling Using Entropy Maximization

In a multithreaded execution environment, race condition leads to computational errors and system hazards. Up to date, a series of task scheduling strategies have been presented in the literature to reduce the risk of race condition. Because of the increasing complexity of this problem in multi-core systems, existing task scheduling approaches are not very efficient. To deal with this challenge, in this work, we develop a race-condition-aware and hardware-oriented task partitioning and scheduling algorithm using entropy maximization model. We model uncertainty as a probabilistic occurrence within a time interval, and hence the characteristics of event ordering are analyzed through an uncertainty matrix. Next, a metric is developed to measure the uncertainty of task execution in various execution environments. Finally, a maximum entropy model is generated to ensure the lowest probability of race condition during task execution. The smallest one among maximum entropy values is chosen and used in our proposed task scheduling algorithm. Experimental results show that the proposed task scheduling strategy based on our maximum entropy model outperforms existing state-of-the-art approaches. For example, in a 128-core computing system, the task execution time, CPU utilization ratio, and throughput of our proposed task scheduling is improved by <inline-formula> <tex-math notation="LaTeX">$15.3\sim 36.4$</tex-math><alternatives><inline-graphic xlink:href="li-ieq1-2784829.gif"/> </alternatives></inline-formula> percent, <inline-formula><tex-math notation="LaTeX">$8.2\sim 17.6$</tex-math> <alternatives><inline-graphic xlink:href="li-ieq2-2784829.gif"/></alternatives></inline-formula> percent, and <inline-formula><tex-math notation="LaTeX">$20.7\sim 41.4$</tex-math><alternatives> <inline-graphic xlink:href="li-ieq3-2784829.gif"/></alternatives></inline-formula> percent, respectively. Moreover, our proposed scheduling algorithm exhibits low computational complexity and good adaptivity to diverse execution environments.