Elsawwaf Ali M, Aly Gamal M, Faheem Hossam M, Fayez Mahmoud
Computer and Systems Engineering Dept, Faculty of Engineering, Ain Shams University, Cairo, Egypt.
Computer systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
Sci Rep. 2024 Nov 1;14(1):26356. doi: 10.1038/s41598-024-75711-8.
Rapid development realms of parallel architectures and its heterogeneity have inspired researchers to invent new scheduling strategies to efficiently distribute workloads among these architectures in a way that may lead to better performance. This paper presents a comprehensive study on optimizing resource utilization for large-scale problems by employing architecture-aware scheduling techniques. We conducted a series of experiments to measure the execution times of various architectures with different problem sizes. These experiments have been conducted multiple times to minimize measurement variance. The findings from these experiments are utilized to develop a scheduling strategy that enables faster completion of larger data-parallel problems while maximizing resource utilization. The proposed approach makes performance enhancement with 16.7% for large data size. It has a significant impact on enhancing computational efficiency and reducing costs in high-performance computing environments.
并行架构的快速发展领域及其异构性激发研究人员发明新的调度策略，以便以可能带来更好性能的方式在这些架构之间高效地分配工作负载。本文通过采用架构感知调度技术，对大规模问题的资源利用优化进行了全面研究。我们进行了一系列实验，以测量不同问题规模下各种架构的执行时间。这些实验已多次进行，以尽量减少测量方差。利用这些实验的结果来开发一种调度策略，该策略能够在最大化资源利用的同时更快地完成更大的数据并行问题。对于大数据规模，所提出的方法使性能提高了16.7%。它对提高高性能计算环境中的计算效率和降低成本具有重大影响。
