Actionable reporting of CPU-GPU performance comparisons: Insights from a CLUBB case study

Abstract. Graphics Processing Units (GPUs) are becoming increasingly central to high-performance computing (HPC), but fair comparison with central processing units (CPUs) remains challenging, particularly for applications that can be subdivided into smaller workloads. Traditional metrics such as speedup ratios can overstate GPU advantages and obscure the conditions under which CPUs are competitive, as they depend strongly on workload choice. We introduce two peak-based performance metrics, the Peak Ratio Crossover (PRC) and the Peak-to-Peak Ratio (PPR) which provide clearer comparisons by accounting for the best achievable performance of each device. Using a case study into the performance of the Cloud Layers Unified by Binormals (CLUBB) standalone model, we demonstrate these metrics in practice, show how they can guide execution strategy, and examine how they shift under factors that affect workload. We further analyze how implementation choices and code structure influence these metrics, showing how they enable performance comparisons to be expressed in a concise and actionable way, while also helping identify which optimization efforts should be prioritized to meet different performance goals.