2024/01/09 by João B. Fernandesa, Fernandes, João B., Ítalo A. S. Assis +7 · 1 citation
Computer Science · #Asynchronous communication #Cloud Computing and Resource Management #Computer network #Computer science #Distributed #Distributed and Parallel Computing Systems #Distributed computing #FOS: Computer and information sciences #Homogeneous #Implementation #IoT and Edge/Fog Computing #Load balancing (electrical power) #Operating system #Overhead (engineering) #Parallel #Process (computing) #Scheduling (production processes) #Software engineering #Symmetric multiprocessor system #Task (project management) #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2401.04494
openalex publication_date 2024/01/09 · openalex created_date 2024/01/13 · openalex updated_date 2026/07/28
Supercomputers have revolutionized how industries and scientific fields process large amounts of data. These machines group hundreds or thousands of computing nodes working together to execute time-consuming programs that require a large amount of computational resources. Over the years, supercomputers have expanded to include new and different technologies characterizing them as heterogeneous. However, executing a program in a heterogeneous environment requires attention to a specific aspect of performance degradation: load imbalance. In this research, we address the challenges associated with load imbalance when scheduling many homogeneous tasks in a heterogeneous environment. To address this issue, we introduce the concept of adaptive asynchronous work-stealing. This approach collects information about the nodes and utilizes it to improve work-stealing aspects, such as victim selection and task offloading. Additionally, the proposed approach eliminates the need for extra threads to communicate information, thereby reducing overhead when implementing a fully asynchronous approach. Our experimental results demonstrate a performance improvement of approximately 10.1% compared to other conventional and state-of-the-art implementations.