🤖 AI Summary
This study addresses the issue of metric failure and result bias caused by missing data in performance portability evaluations for high-performance computing (HPC). We systematically assess the effectiveness of multiple imputation algorithms in handling missing performance efficiency data. Leveraging real-world HPC benchmark datasets, we conduct an in-depth comparative analysis of these methods across three dimensions: accuracy, robustness, and computational cost, elucidating their applicable scenarios and inherent trade-offs. This work delineates the boundaries of strengths and limitations among different imputation strategies, providing concrete practical guidance and methodological support for mitigating the impact of missing data on performance portability analysis.
📝 Abstract
Missing data is a common challenge in many real-world datasets, often leading to biased results or reduced accuracy. Missing data is a pervasive problem in performance efficiency datasets, particularly in high-performance computing (HPC) performance portability studies. Performance portability scores rely on complete sets of performance efficiencies as inputs to their respective metrics; however, missing values can arise due to incomplete benchmarking, hardware constraints, or implementation gaps. Many established performance portability metrics lack mechanisms for handling missing inputs, which can result in incomplete analyses or the inability to compute scores altogether. In this study, we evaluate a range of imputation methods and algorithms for addressing missing performance efficiencies. We present illustrative examples for each approach and apply them to real-world HPC performance portability datasets. Our results highlight the trade-offs between accuracy, robustness, and computational cost, offering practical recommendations for researchers seeking to mitigate the impact of missing data in performance portability assessments.