HPC

High-performance computing (HPC) refers to the use of supercomputers and parallel processing techniques to solve complex computational problems that are too large for conventional computers. HPC systems typically consist of many CPUs, accelerators, or both, connected by high-bandwidth networks, and are used for simulations, data analysis, and scientific research across fields such as climate, physics, chemistry, and engineering.

Architectures combine distributed memory machines (clusters) and shared memory within nodes. Modern HPC often employs accelerators like GPUs, with heterogeneous computing. Interconnects (InfiniBand, Ethernet) and fast storage enable scalable performance. Performance is measured in FLOPS; peak versus sustained performance; workload-optimized architecture.

Software in HPC centers on parallel programming models and libraries. Parallel programming models include MPI for interprocess communication, OpenMP for shared memory, and accelerator programming environments such as CUDA and OpenCL. Job schedulers (SLURM, PBS, Grid Engine) manage resource allocation. Common libraries include BLAS, LAPACK, MPI, and domain-specific tools. Applications span weather forecasting, computational fluid dynamics, materials science, genomics, and machine learning at scale.

Trends in HPC include the pursuit of exascale computing, aiming for at least 10^18 floating-point operations per second. Challenges include energy efficiency, reliability, data movement, and programming complexity. Systems increasingly rely on heterogeneous hardware, deep memory hierarchies, and advanced cooling. Data management, software ecosystems, and reproducibility are evolving to support larger datasets and collaborative research across disciplines.