/ˌeɪtʃ piː ˈsiː/
n. "Parallel computing clusters solving complex simulations via massive CPU/GPU node aggregation unlike single workstations."
HPC is the practice of aggregating thousands of compute nodes with high-speed interconnects to perform massively parallel calculations—SerDes 400G fabrics and NVLink 900GB/s link HBM3 memory to 128-GPU SXM blades solving CFD/climate models infeasible on desktops. Exascale systems like Frontier deliver 1.2 exaFLOPS via 3D torus networks where MPI distributes domains across nodes while NCCL handles intra-GPU tensor parallelism.
Key characteristics of HPC include:
- Cluster Architecture: 100K+ nodes with InfiniBand 400Gbps NDR or NVLink domains.
- Memory Bandwidth: HBM3 3TB/s/node feeds FP64 tensor cores for CFD/ML.
- Parallel Frameworks: MPI+OpenMP+CUDA partition domains across socket/GPU/accelerator.
- Scaling Efficiency: 80-95% weak scaling to 100K cores before communication bounds.
- Power Density: 60kW/rack liquid-cooled; PUE <1.1 via rear-door heat exchangers.
A conceptual example of HPC CFD workflow:
1. Domain decomposition: 1B cells → 100K partitions via METIS
2. MPI_Dims_create(1000,100,1) → 3D Cartesian topology
3. Each rank solves 10M-cell NS equations w/ RK4 timestep
4. NVLink halo exchange 1GB/iteration <10μs latency
5. Global residual reduction every 100 steps MPI_Allreduce
6. Checkpoint HBM3 → Lustre 2TB/s every 1000 iterationsConceptually, HPC is like an ant colony tackling a mountain—millions of tiny processors collaborate via fast chemical signals (SerDes/NVLink) solving problems individually impossible, from weather prediction to PAM4 signal integrity simulation.
In essence, HPC powers exascale science from fusion plasma modeling to Bluetooth 6G PHY optimization, crunching petabytes through DQS-DDR5+HBM3 fed by ENIG backplanes while mitigating EMI in dense racks.