HPC/Simulation Workloads
High-Performance Computing (HPC) workloads are large-scale simulations, modeling, and data analysis tasks that require tightly coupled compute and storage. Unlike AI workloads, which emphasize tensor throughput and model optimization, HPC focuses on numerical precision, interconnect latency, and parallel scaling. HPC is the foundation for climate science, materials discovery, genomics, physics, and industrial design — and it increasingly overlaps with AI in hybrid workflows.
Overview
- Purpose: Run scientific and engineering simulations at scale — from weather forecasting to molecular dynamics.
- Scale: Dozens to tens of thousands of nodes; 1–100 MW+ deployments in national labs and supercomputing centers.
- Characteristics: MPI/SHMEM-based communication, batch scheduling, checkpoint/restart, floating-point intensity.
- Comparison: HPC emphasizes tightly coupled nodes and deterministic communication, while AI emphasizes massive parallelism with tolerable slack in synchronization.
Common Workloads
- Climate & Weather: Global circulation models, hurricane prediction, climate risk simulations.
- Materials & Chemistry: Quantum simulations, drug discovery, computational chemistry.
- Genomics & Biology: Genome sequencing, protein folding, epidemiological models.
- Physics & Engineering: CFD (aerospace, automotive), nuclear physics, astrophysics.
- Energy & Industry: Seismic imaging, oil & gas reservoir modeling, fusion experiments.
Bill of Materials (BOM)
| Domain | Examples | Role |
|---|---|---|
| Compute Nodes | AMD EPYC, Intel Xeon, Fujitsu A64FX, NVIDIA GH200 | High-core CPUs with some GPU acceleration |
| Accelerators | NVIDIA A100/H100, AMD MI250X, Intel Ponte Vecchio | GPU acceleration for hybrid HPC+AI workloads |
| Interconnect | InfiniBand NDR, HPE Slingshot, Cray Aries | Ultra-low latency communication for MPI workloads |
| Storage | Lustre, BeeGFS, IBM Spectrum Scale (GPFS), DDN | Parallel file systems for checkpointing and data ingest |
| Schedulers | Slurm, PBS Pro, LSF | Batch job orchestration for multi-user environments |
| Cooling | Direct-to-chip liquid cooling, immersion | Required for dense CPU+GPU racks |
Facility Alignment
| Workload Mode | Best-Fit Facilities | Also Runs In | Notes |
|---|---|---|---|
| National-Scale Simulation | Supercomputers (Frontier, Aurora, Fugaku) | Hyperscale (hybrid AI/HPC) | Exaflop-class, government funded |
| Academic/Consortium HPC | University HPC clusters | Colo (specialized racks) | Shared scientific resources |
| Enterprise HPC | Enterprise DCs, Colo | Cloud (elastic HPC) | Industrial simulations, oil & gas, manufacturing |
Key Challenges
- Energy Demand: Supercomputers require 20–100 MW, stressing regional grids.
- Interconnect Scaling: Maintaining µs-level latency across tens of thousands of nodes.
- Storage Throughput: Checkpoint/restart cycles demand multi-TB/s bandwidth.
- Fault Tolerance: Long jobs must survive hardware failures; checkpoint/restart is essential.
- Workload Diversity: Scientific users have competing priorities; schedulers must balance fairness and efficiency.
- Talent: Shortage of HPC engineers, MPI programmers, and operators.
Notable Deployments
| System | Operator | Performance | Notes |
|---|---|---|---|
| Frontier | Oak Ridge National Lab (US) | 1.1 EF | First exascale-class HPC |
| Aurora | Argonne National Lab (US) | Exascale-class | Intel GPU-based hybrid HPC/AI |
| Fugaku | RIKEN, Japan | 442 PF | ARM-based CPUs, scientific workloads |
| LUMI | CSC, Finland (EuroHPC) | 375 PF | Green HPC, hydro-powered |
Future Outlook
- Hybrid AI+HPC: Simulation accelerated by AI surrogates and generative models.
- Exascale Expansion: More exaflop systems in US, EU, China, Japan by 2027–2030.
- Green HPC: Renewables, nuclear co-siting, liquid cooling, and carbon-aware scheduling.
- Federated HPC: Linking clusters globally into cooperative compute grids.
- Quantum Integration: Early coupling of quantum accelerators with HPC for hybrid workflows.
FAQ
- How does HPC differ from AI training? HPC emphasizes precise numerical simulations and MPI interconnects; AI training emphasizes tensor throughput and gradient updates.
- Where does HPC usually run? Government supercomputers, academic clusters, and industrial HPC in colos/enterprise DCs.
- Why is HPC so energy-hungry? Thousands of tightly coupled nodes run at full utilization for weeks; energy is a fixed baseload.
- Can HPC run in the cloud? Yes, via elastic HPC instances, but performance is lower due to interconnect limits.
- What’s the future of HPC? Hybrid AI+simulation, exascale-class expansion, and integration with quantum and green energy sources.