For complex engineering and scientific applications, Computational Fluid Dynamics (CFD) simulations require a huge amount of computational power. As such, it is of paramount importance to carefully assess the performance of CFD codes and to study them in depth for enabling optimisation and portability. In this paper, we study three complex CFD codes, OpenFOAM, Alya and CHORUS representing two numerical methods, namely the finite volume and finite-element methods, on both structured and unstructured meshes. To all codes, we apply a generic performance analysis method based on a set of metrics helping the code developer in spotting the critical points that can potentially limit the scalability of a parallel application. We show the root cause of the performance bottlenecks studying the three applications on the MareNostrum4 supercomputer. We conclude providing hints for improving the performance and the scalability of each application.

Computing technologies populating high-performance computing (HPC) clusters are getting more and more diverse, offering a wide range of architectural features. As a consequence, efficient programming of such platforms becomes a complex task. In this paper we provide a micro-benchmarking of three HPC clusters based on different CPU architectures, predominant in the Top500 ranking: x86, Armv8 and IBM Power9. On these platforms we study a production fluid-dynamics application leveraging different compiler technologies and micro-architectural features. We finally provide a scalability study on state-of-the-art HPC clusters. The two most relevant conclusions of our study are: i) Compiler development is critical for squeezing performance out of most recent technologies; ii) Micro-architectural features such as Single Instruction Multiple Data (SIMD) units and Simultaneous Multi-Threading (SMT) can impact the overall performance. However, a closer look shows that while SIMD is improving the performance of compute bound regions, SMT does not show a clear benefit on HPC workloads.

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.