Evaluating Paramount Iterations as a Performance Proxy for High Performance Computing Applications in the Cloud

Abstract

Cloud computing has transformed data storage, access, and processing, offering significant opportunities and advantages. However, selecting the most cost‐effective and efficient cloud resources for high‐performance computing (HPC) workloads remains a challenge, due to their long‐running and resource‐intensive characteristics that require tailored allocation strategies. In this work, a methodology is presented to optimize cloud resource selection for HPC applications using a performance proxy based on a few Paramount Iterations (PIs). The proxy enables the estimation of Relative Performance and Relative Cost between two cloud configurations with minimal overhead, allowing different optimization strategies to identify configurations that minimize execution time or cost. This approach extends previous work by evaluating a broader range of applications and cloud configurations, systematically identifying limitations and highlighting scenarios where the technique is most effective. The methodology was evaluated across three cloud providers, and results show that selecting instances based on the second PI achieves at least 97% of the best achievable execution time in most cases, while highlighting cases where additional PIs improve selection accuracy. These findings confirm that the proposed methodology effectively predicts optimal cloud resources for HPC workloads, reducing verification costs and providing a practical framework for informed resource selection.