Performance modeling of the HPCG benchmark

Abstract

The TOP 500 list is the most widely regarded ranking of modern supercomputers, based on Gflop/s measured for High Performance LINPACK (HPL). Ranking the most powerful supercomputers is important: Hardware producers hone their products towards maximum benchmark performance, while nations fund huge installations, aiming at a place on the pedestal. However, the relevance of HPL for realworld applications is declining rapidly, as the available compute cycles are heavily overrated. While relevant comparisons foster healthy competition, skewed comparisons foster developments aimed at distorted goals. Thus, in recent years, discussions on introducing a new benchmark, better aligned with real-world applications and therefore the needs of real users, have increased, culminating in a highly regarded candidate: High Performance Conjugate Gradients (HPCG). In this paper we present an in-depth analysis of this new benchmark. Furthermore, we present a model, capable of predicting the performance of HPCG on a given architecture, based solely on two inputs: the effective bandwidth between the main memory and the CPU and the highest occuring network latency between two compute units. Finally, we argue that within the scope of modern supercomputers with a decent network, only the first input is required for a highly accurate prediction, effectively reducing the information content of HPCG results to that of a stream benchmark executed on one single node. We conclude with a series of suggestions to move HPCG closer to its intended goal: a new benchmark for modern supercomputers, capable of capturing a well-balanced mixture of relevant hardware properties.