HighPerformanceNetzwerkkarten

High-performance computing (HPC) refers to the use of supercomputers and parallel processing techniques to run large-scale computations more quickly than conventional systems. HPC enables complex simulations, data analysis, and modeling across science, engineering, and industry. Systems range from clusters of commodity servers to large-scale supercomputers with millions of cores. Architectures typically feature multi-core CPUs and accelerators such as GPUs or FPGAs, connected by high-bandwidth networks and supported by parallel file systems and job schedulers.

Workloads are typically parallelizable, using distributed memory models with message passing (MPI) and shared memory paradigms (OpenMP) or accelerator-aware approaches (CUDA, OpenCL). Common HPC platforms run Linux-based operating environments with workload managers like Slurm or PBS and libraries such as BLAS, LAPACK, and PETSc. Data storage relies on parallel file systems such as Lustre or GPFS and high-performance interconnects like InfiniBand.

Historically, HPC has evolved from vector supercomputers to massively parallel clusters in the 1990s, moving toward petascale systems in the 2000s and now pursuing exascale performance. Applications include climate modeling, computational chemistry, astrophysics, genomics, and engineering simulations; in recent years, AI training and data analytics have become common concurrent workloads.

Challenges include energy efficiency, fault tolerance, programming complexity, and software portability across architectures. Trends emphasize exascale development, heterogeneous computing with GPUs and AI accelerators, cloud-based HPC offerings, and data-centric optimization.