Designing an HPC Cluster for CFD and FEA Applications:

For CFD and FEA workloads, cluster design is not just about adding more cores. It is about matching hardware to solver behavior, building the right node types, sizing memory correctly, choosing the right network and storage, and then managing the entire environment in a way that is easy for users and efficient for administrators.

This is especially important because CFD and FEA users do not consume infrastructure in the same way.

CFD workloads often demand strong parallel scaling, high memory bandwidth, and fast node-to-node communication. FEA workloads, particularly structural and implicit solves, often become more sensitive to memory capacity, memory bandwidth, and I/O behavior of storage as model size grows.

That means a well-designed HPC cluster for engineering cannot be built as a one-size-fits-all compute pool. It should be designed around user profiles, workload classes, bill of materials, procurement discipline, and operational policy with SyncHPC acting as the management layer that turns hardware into a usable engineering platform.

Design separate hardware pools for CFD and FEA

Instead of buying identical nodes for all simulation users, a more efficient design is to build node classes.

CFD Node profile

CFD nodes should prioritize:

High core density with strong memory bandwidth
If solver supports GPU compute, then have relevant GPUs like NVIDIA H100/H200/B300 etc.
Fast and low-latency interconnect (InfiniBand)
Balanced RAM per core
Strong MPI performance

For cluster-scale CFD, low-latency networking matters because distributed-memory jobs rely on frequent communication between nodes. NVIDIA’s InfiniBand platform is built specifically around ultra-low latency and high-throughput HPC communication.

FEA Node profile

FEA nodes should prioritize:

Higher memory capacity per node
If solver supports GPU compute, then have relevant GPUs like NVIDIA H200/B300 etc.
Strong memory bandwidth
Fewer but more effective cores per job when solver scaling tapers
Fast “local scratch” and robust storage throughput

This is particularly relevant for large implicit, structural, or multiphysics models where memory pressure and disk I/O can become the real limiters. In some cases with availability of more physical memory, simulations can avoid disk I/O and run much more efficiently.

Build the cluster with following Checklist

Below is a reference checklist for a cluster supporting mixed CFD and FEA usage. This is not a final procurement checklist; it is a practical starting point.

Reference BOM: Mid-sized CFD + FEA cluster

A. CFD Compute Pool:

Make sure the the CFD compute pool is according to the CFD node profile.

B. FEA Compute Pool

Make sure the the FEA compute pool is according to the CFD node profile.

C. Optional GPU Pool

GPU nodes for Compute:
- At the time of writing this post, some of the leading CFD and even FEA solvers already support GPU solver for specific use cases
- In future, multiple solvers will support GPU based simulation. Hence, it is recommended to have at least few nodes with GPU solving capacity (like NVIDIA H200/B300 etc.)
GPU nodes for Visualization (pre/post processing):
- Sometimes having high-end workstations for every user may not be feasible.
- Users need to download large files to their respective workstations after simulation. This can be avoided with ‘remote visualisation nodes’.
- Remote visualization avoids data copy and data duplication. It also keeps data at centralized location.
- It is recommended to use Nvidia RTX Pro 6000 BSE or NVIDA L40s for visualisation nodes.

D. Storage

High-performance shared file system backed by SSD / NVMe disks.
For large clusters with more than 10 compute nodes, it is recommended to have Parallel File System like Lustre.
The capacity of storage can be defined by understanding the required storage by all users for next 5 years.
But, generally 80% of this data will be a raw data. Hence, it can be deleted at regular intervals (say monthly). Also, the capacity planning should be done based on this understanding.

E. Networking

High-speed InfiniBand switch fabric for compute traffic is required.
Ethernet switch(s) for management traffic

F. Rack and power

Rack enclosure
PDUs
UPS integration
Cooling planning
KVM / console access

This type of BOM works because it reflects actual workload behavior: CFD benefits more from fast parallel communication and balanced cores-per-node, while FEA often needs larger memory footprints and more conservative sizing per job. That balance is consistent with Ansys guidance on compute-memory-I/O balance and with HPC networking guidance for tightly coupled distributed workloads.

Buy the Correct BOM

Many HPC projects fail at the procurement stage because the BOM is technically complete but operationally wrong.

A correct BOM should be purchased only after validating:

Which CFD and FEA solvers will run
Typical mesh size or model size
Average and peak concurrent users
License limits
Whether jobs are mostly single-node or multi-node
Expected growth over 3-5 years
Storage retention policy
Remote visualization needs

For example, buying only dense CPU nodes may look cost-efficient, but it can become a poor fit if FEA users routinely require high memory per job. Likewise, buying large-memory nodes only can be wasteful if most CFD users need scalable MPI throughput rather than oversized RAM.

A better procurement process is:

Profile workloads
Define node classes
Benchmark representative cases
Freeze BOM
Purchase in phases if growth is uncertain

Set Usage Policy: Memory per CPU Core Matters

One of the most practical decisions in HPC design is deciding how much memory per CPU core should be available and enforced.

This matters because adding more cores does not always improve simulation time if memory per core drops too low. Ansys notes that each processor core requires additional memory bandwidth, and that balanced CPU-memory-I/O design is critical for performance.

A practical operating policy

As a starting rule for cluster operations:

CFD queue: reserve roughly 4–8 GB RAM per CPU core
FEA queue: reserve roughly 8–16 GB RAM per CPU core
large-memory FEA jobs: schedule onto dedicated high-memory-nodes
GPU CFD jobs: size around GPU memory and supported solver workflow, not just CPU core count

Those numbers are practical planning heuristics, not universal laws. They should be tuned after benchmark runs on real customer models.

In other words:

Too few cores can make jobs slow
Too many cores can waste license, create communication overhead, and reduce efficiency
Too little memory per core can push jobs into swapping or heavy I/O

The right answer is not “maximum cores,” but balanced cores, memory, and solver fit.

Build the Cluster Correctly

After procurement, cluster build-out should follow a structured path:

Physical build

Rack and cable all nodes
Separate management and compute fabrics
Configure power redundancy
Validate cooling and airflow

Software build

Install OS image across nodes
Configure scheduler
Configure MPI and fabric drivers
Mount shared storage
Install solver environments
Set up license connectivity
Implement monitoring and alerting

Validation

Run network tests
Run storage throughput tests
Run single-node and multi-node solver benchmarks
Validate queue policies
Validate user access and permissions

The objective is not just to make the cluster operational, but to make it predictable for engineering teams.

Where SyncHPC Fits In

Once the hardware is in place, the real operational challenge begins: how to make this environment easy to consume. This is where SyncHPC becomes critical.

SyncHPC sits above the infrastructure layer and helps turn a hardware cluster into a managed engineering platform. Instead of users dealing directly with fragmented scripts, queues, manual policies, and backend complexity, SyncHPC provides a unified way to manage simulation resources across the environment.

How SyncHPC helps after the cluster is built

User-specific access

CFD users and FEA users can be given different access paths, queues, templates, and policies based on workload type.

Queue and node-class mapping

SyncHPC can map:

CFD users to CFD nodes
FEA users to high-memory-nodes
GPU users to GPU-enabled resources
priority users to reserved partitions or policies

Policy-based scheduling

Memory-per-core policies, project quotas, team-level restrictions, and workload classes can be standardized instead of manually enforced.

Unified job submission

Users can submit, monitor, and manage jobs through a centralized interface rather than dealing with fragmented infrastructure operations.

Hybrid extension

If on-prem resources are full, organizations can extend selectively into cloud resources while maintaining a unified management layer. So, while the BOM and hardware design determine the raw capability of the cluster, SyncHPC determines how effectively that capability is delivered to engineering teams.

Conclusion

Designing an HPC cluster for CFD and FEA applications is a hardware problem first, but not a hardware problem only.

It requires:

Identifying CFD and FEA user behavior separately
Building the right node types
Sizing memory per CPU core correctly
Purchasing the right BOM
Validating build quality with benchmarks
Managing the entire environment with a platform that keeps operations efficient

That is the real value of SyncHPC. It does not replace the need for a well-designed cluster. It makes that cluster usable, governable, and scalable for real engineering teams.