Supercomputer HEMUS: Benchmark Results and Performance Optimization

Supercomputers and advanced High-Performance Computing (HPC) systems are needed to address large scientific problems, AI models training or industrial/engineering tasks. One of the important steps during the installation, configuration and tuning stages, is to perform comprehensive benchmarking and obtain insights about the best ways of exploiting the systems capabilities. HEMUS is modern petascale heterogeneous HPC system. Benchmarking insights and optimization strategies from HEMUS are applicable to a broad class of contemporary HPC systems. In this paper we present results from general benchmarks, together with optimization strategies applied and the obtained conclusions. We also present benchmarks related to software simulation of quantum computing algorithms, as well as simulation using low-discrepancy sequences, as they cover a significant portion of the expected workload on the system. The results highlight the importance of careful tuning and optimization, demonstrating that significant performance gains can be achieved, with the proposed approaches broadly applicable across comparable HPC systems and workloads.