Clusters of emerging technologies are appearing with more and more frequency in HPC. After years of skepticism, data-centers are adopting them as production systems thanks to several geopolitical and technological factors. The most honorable example is the Fugaku supercomputer, powered by the latest Fujitsu A64FX CPU. Which is the behavior of mature HPC codes on such emerging technology clusters? Which performance will obtain scientists when running their HPC applications “as is” on these clusters? This paper presents the evaluation of CTE-Arm, a Fugaku-like system, including both fine-tuned micro-benchmarks and five scientific applications run without prior fine-tuning: Alya, NEMO, Gromacs, OpenIFS, and WRF. Results show that while micro-architectural benchmarks show performance as expected, the performance obtained running HPC applications not tuned for a specific architecture are between $2\times $ and $4\times $ slower compared with a standard Intel-based HPC system. Therefore further effort is needed to improve tools (e.g., compilers) and system software (e.g., MPI libraries) to ease applications deployment and improve their performance.

HPC systems and parallel applications are increasing their complexity. Therefore the possibility of easily study and project at large scale the performance of scientific applications is of paramount importance. In this paper we describe a performance analysis method and we apply it to four complex HPC applications. We perform our study on a pre-production HPC system powered by the latest Arm-based CPUs for HPC, the Marvell ThunderX2. For each application we spot inefficiencies and factors that limit their scalability. The results show that in several cases the bottlenecks do not come from the hardware but from the way applications are programmed or the way the system software is configured.