Does every provider's HPC offering support the same interconnect bandwidth?

No, and this is the single biggest practical differentiator in this category. AWS's EFA reaches up to 3,200 Gbps on P5 instances, Azure's InfiniBand HDR/NDR runs 200-400 Gbps, GCP's Jupiter fabric with GPUDirect-TCPXO targets A3 GPU instances specifically, and OCI's RDMA cluster networking runs at 100 Gbps. For a communication-bound MPI workload, this difference directly affects scaling efficiency at high node counts, benchmark your actual workload's communication pattern rather than assuming interconnect specs are roughly interchangeable.

Can I use a standard Slurm workflow across all four providers without rewriting job scripts?

Largely yes at the job-script level since Slurm itself is largely provider-agnostic, but the surrounding cluster provisioning and node-image configuration differs (AWS ParallelCluster's config format, Azure CycleCloud's cluster templates, GCP HPC Toolkit's blueprint format), so while your srun/sbatch scripts port cleanly, the infrastructure-as-code layer defining the cluster itself needs a provider-specific rewrite.

Is bare-metal HPC compute (like OCI's HPC shapes) meaningfully different from virtualized instances for performance?

Yes, for latency-sensitive or noisy-neighbor-sensitive HPC workloads, bare metal eliminates hypervisor overhead and shares nothing with co-tenants, which matters for consistent, reproducible benchmark results in fields like computational chemistry or weather modeling where run-to-run variance itself is a problem, not just average throughput.

Multi-Cloud HPC Compare

ComputeMulti-Cloud

Compare HPC compute offerings across AWS ParallelCluster, Azure CycleCloud, GCP HPC Toolkit, and OCI HPC.

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What This Tool Does

A comparison of HPC-oriented compute, interconnect, and job scheduling across AWS (ParallelCluster/Batch/EFA), Azure (CycleCloud/InfiniBand HB-HC-ND series), GCP (HPC Toolkit/Cloud Batch), and OCI (HPC bare-metal shapes/RDMA cluster networking), and the interconnect technology is the detail that actually determines whether a tightly-coupled MPI workload will scale: AWS's EFA delivers up to 3,200 Gbps on P5 instances via a non-blocking fabric, Azure uses InfiniBand HDR/NDR at 200-400 Gbps for its HB/ND series, GCP uses its Jupiter fabric plus GPUDirect-TCPXO specifically for A3 GPU instances, and OCI uses 100 Gbps RoCE v2 RDMA cluster networking, genuinely different numbers that matter for a workload bottlenecked on inter-node communication rather than raw compute.

Technical Details

The comparison table is a static, hand-maintained dataset of feature rows grouped by category (overview, compute, networking, storage, pricing) with free-text search across all fields; it's a reference snapshot, not a live specs or pricing feed, since HPC instance families and interconnect generations change relatively often, verify current generation and bandwidth figures directly with the provider before a procurement decision.

Common Use Cases

1Matching a tightly-coupled MPI workload (CFD, molecular dynamics) to the provider whose interconnect bandwidth and topology (EFA, InfiniBand, Jupiter/GPUDirect-TCPXO, RDMA cluster networking) actually fits the workload's communication pattern, rather than picking a provider on raw vCPU count alone.
2Choosing between a managed scheduler (AWS ParallelCluster's Slurm, Azure CycleCloud's Slurm/PBS/Grid Engine support, GCP HPC Toolkit's Slurm/HTCondor) versus rolling a custom scheduler deployment, based on which job scheduler a research team already has existing job scripts and workflow tooling built around.
3Selecting HPC-optimized parallel filesystem options (FSx for Lustre, Azure Managed Lustre, GCP Parallelstore, OCI File Storage) appropriately sized for an I/O-bound workload instead of defaulting to general-purpose object or block storage that can't sustain the required throughput.
4Evaluating spot/preemptible support for HPC workloads (all four support it in some form) against a specific job's checkpoint-restart tolerance, since interrupting a long-running tightly-coupled MPI job without checkpointing can waste far more compute than the spot discount saves.

Common Questions

Does every provider's HPC offering support the same interconnect bandwidth?: No, and this is the single biggest practical differentiator in this category. AWS's EFA reaches up to 3,200 Gbps on P5 instances, Azure's InfiniBand HDR/NDR runs 200-400 Gbps, GCP's Jupiter fabric with GPUDirect-TCPXO targets A3 GPU instances specifically, and OCI's RDMA cluster networking runs at 100 Gbps. For a communication-bound MPI workload, this difference directly affects scaling efficiency at high node counts, benchmark your actual workload's communication pattern rather than assuming interconnect specs are roughly interchangeable.
Can I use a standard Slurm workflow across all four providers without rewriting job scripts?: Largely yes at the job-script level since Slurm itself is largely provider-agnostic, but the surrounding cluster provisioning and node-image configuration differs (AWS ParallelCluster's config format, Azure CycleCloud's cluster templates, GCP HPC Toolkit's blueprint format), so while your srun/sbatch scripts port cleanly, the infrastructure-as-code layer defining the cluster itself needs a provider-specific rewrite.
Is bare-metal HPC compute (like OCI's HPC shapes) meaningfully different from virtualized instances for performance?: Yes, for latency-sensitive or noisy-neighbor-sensitive HPC workloads, bare metal eliminates hypervisor overhead and shares nothing with co-tenants, which matters for consistent, reproducible benchmark results in fields like computational chemistry or weather modeling where run-to-run variance itself is a problem, not just average throughput.

Expert Tips

TIP

Interconnect bandwidth numbers in vendor marketing are peak, theoretical figures, always validate actual achievable bandwidth and latency for your specific instance count and placement group configuration with a real benchmark (like OSU MPI benchmarks) before sizing a production HPC cluster around the headline number.

TIP

HPC-optimized parallel filesystems (Lustre-based options especially) have real minimum provisioned throughput/capacity tiers that get expensive fast, size storage to the workload's actual I/O pattern rather than defaulting to the largest tier out of caution.

TIP

A checkpoint-restart strategy is close to mandatory for using spot/preemptible capacity on any long-running tightly-coupled HPC job, without it, a single interruption late in a multi-day MPI run can waste the entire run's compute rather than just the interrupted node's share.

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