SciLance: Mitigate Load Imbalance for Parallel Scientific Applications in Cloud Environments
Xinying Wang, Lipeng Wan, Scott Klasky, Dongfang Zhao, Feng Yan · 2023
Elastic cloud computing provides new opportunities for accelerating the process of scientific discovery. However, unlike high-performance computing (HPC) systems that are built and optimized for workloads with intensive inter-node communication demands, the low-latency and high bandwidth communication capability is only enabled on a few very expensive high-end instance types in the cloud, which leads to poor cost-effectiveness. In addition, re-balancing the workload through extra data movement among compute nodes is a common way to mitigate the load imbalance issue in many scientific simulations, which further amplifies the communication pressure and makes it challenging to efficiently use cloud resources. To this end, we propose SciLance, which addresses the workload imbalance challenge by utilizing the heterogeneous and elastic resources offered by cloud platforms. Particularly, instead of moving data excessively among compute instances to balance the workload, SciLance dynamically adjusts the computer instances used for running parallel tasks based on the runtime imbalance identified through profiling. We prototype SciLance and perform extensive evaluation using adaptive mesh refinement (AMR) based scientific applications. The evaluation results demonstrate that SciLance can achieve up to 36.63% better performance with 16.91% lower cost for AMR-based simulation codes.