Designing HPC Cluster Networking: What Speeds You Actually Need When building or scaling an HPC cluster, CPUs and GPUs usually get most of the attention. But in practice, the network design is just as critical. A poorly designed network can bottleneck even the most powerful compute nodes, while a well designed one can significantly improve performance without changing hardware. This guide breaks down typical networking components in an HPC cluster and what speeds are generally recommended between them. ⸻ Why Networking Matters in HPC In HPC environments, nodes rarely work in isolation. They constantly exchange data for: MPI communication Distributed AI/ML training Accessing shared storage If the network cannot keep up, nodes spend time waiting instead of computing. ⸻ Key Network Paths in an HPC Cluster Let’s break the cluster into major communication paths: Compute Node ↔ Compute Node (Interconnect) Compute Node ↔ Storage Login Node ↔ Compute Nodes External Access (Users ↔ Login Node) Each of these has different requirements. ⸻ 1. Compute Node ↔ Compute Node (Interconnect) This is the most critical network in HPC. MPI traffic Synchronization between processes Distributed workloads Minimum: 25 Gbps Common: 100 Gbps High-end: 200–400 Gbps InfiniBand (very low latency) Omni-Path High-speed Ethernet (RoCE, RDMA-enabled) Low latency is more important than raw bandwidth RDMA support is highly recommended Poor interconnect leads to: Poor scaling High communication overhead Underutilized CPUs/GPUs ⸻ 2. Compute Node ↔ Storage Handles: Reading input datasets Writing results Checkpoints Minimum: 10–25 Gbps Typical: 40–100 Gbps High-performance setups: 100+ Gbps NFS (basic setups) Lustre / BeeGFS / GPFS (parallel file systems) Throughput matters more than latency Parallel file systems scale better than NFS If storage is slow: Jobs stall during I/O GPUs sit idle waiting for data ⸻ 3. Login Node ↔ Compute Nodes Role Job submission Monitoring Light data movement 1–10 Gbps is usually sufficient This path is not performance-critical Should be isolated from high-speed compute traffic ⸻ 4. External Access (User ↔ Login Node) Role SSH access File transfers Development workflows Depends on environment Typically 1–10 Gbps uplink Security is more important than speed here Use firewalls, VPNs, and access controls ⸻ Network Design Approaches 1. Single Network (Simple Setup) One network for everything Lower cost Easier to manage Downside: ⸻ 2. Dual Network (Recommended) High-speed network for compute + storage Separate Ethernet network for management Benefits: Better performance Reduced congestion More predictable behavior ⸻ 3. Dedicated Storage Network (Advanced) Separate network just for storage traffic Used in: Large clusters Data-intensive workloads ⸻ Latency vs Bandwidth (Important Distinction) Latency: Time to send a message Bandwidth: Amount of data transferred MPI workloads → sensitive to latency Data-heavy workloads → depend on bandwidth A fast network with high latency can still perform poorly for MPI jobs. ⸻ Common Mistakes in HPC Networking Using standard Ethernet without RDMA for MPI workloads Mixing storage and compute traffic on the same link Underestimating storage bandwidth needs Ignoring network topology (oversubscription issues) Not validating actual performance with benchmarks ⸻ Practical Example Cluster Setup: 16 compute nodes GPU workloads + MPI 100 Gbps InfiniBand for inter-node communication 100 Gbps link to parallel storage 1 Gbps management network Efficient scaling across nodes Reduced job runtime Stable performance under load ⸻ Final Thoughts HPC networking is not just about choosing the fastest hardware. It is about: Matching the network to your workload Separating traffic intelligently Avoiding bottlenecks before they appear In many cases, upgrading or redesigning the network delivers more performance improvement than upgrading CPUs or GPUs. If your cluster is not scaling as expected, the network is often the first place to look.