Robust evaluation of GPU compute instances for HPC and AI in the cloud: a TOPSIS approach with sensitivity, bootstrapping, and non-parametric analysis

Abstract

Evaluating GPU compute instances for High Performance Computing (HPC) and Artificial Intelligence (AI) applications in the cloud involves complex decision-making processes. This research applies the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank various GPU compute instances for HPC and AI from leading cloud providers: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and Oracle Cloud Infrastructure (OCI). The analysis incorporates a sensitivity examination, bootstrapping, and non-parametric tests to ensure robust and reliable rankings. Sensitivity analysis reveals the stability of the TOPSIS framework despite variations in criteria weights, while bootstrap analysis provides confidence intervals for the rankings, highlighting their consistency. The Friedman test confirms that ranking stability persists across different scenarios, indicating minimal impact from weight adjustments. Despite these insights, limitations such as interdependencies among criteria, data accuracy, and generalizability constraints must be acknowledged. This comprehensive approach ensures informed decision-making for selecting optimal GPU instances for cloud-based HPC and AI tasks.