Optimizing License Utilization in High-Performance Engineering Environments

HPC efficiency is usually measured through compute utilization, job throughput, queue times, and storage performance. But engineering workloads depend on another resource: software licenses. A job can have CPU or GPU capacity available and remain blocked because the required application or feature license is unavailable.

The cost of software license resources can be substantial. AWS reports that in electronic design automation (EDA), license costs are often 5–10 times higher than the underlying IT infrastructure cost, even at large organizations with negotiated enterprise agreements.

You can identify real bottlenecks, improve scheduling and sharing, and make more informed renewal and procurement decisions by tracking license demand alongside workload throughput and compute utilization. This way, license optimization becomes part of broader HPC capacity planning rather than a separate software-management task.

This article explores how license visibility, intelligent sharing, and portfolio-level planning can help engineering organizations get more value from their HPC investments.

Why Software Licenses Can Become an HPC Bottleneck

In high-performance engineering environments, software licenses can constrain workload throughput just as directly as compute capacity. A cluster can have available CPUs or GPUs while jobs remain unable to run because the required application or feature license is already in use. It is important to consider whether available compute capacity can actually translate into ready-to-run jobs.

Compute Capacity Does Not Guarantee Job Readiness

Adding more compute capacity does not guarantee that engineering jobs will start faster. Many simulation, CAD, EDA, and analysis workloads depend on commercial applications with finite license pools. A cluster may have CPUs, GPUs, memory, and storage available, while the software licenses required to use them are fully allocated.

Consider a simulation job that requests 64 CPU cores and a solver feature requiring four license tokens. The scheduler may find enough compute resources, but if those four tokens are already in use, the job cannot start. It must wait until the licenses are released. HPC schedulers can treat software licenses as schedulable resources, allowing a job to remain pending until both its compute and license requirements are available.

The problem becomes more significant when license availability varies independently of compute demand. A license pool can reach saturation during a design team’s peak simulation window while the cluster itself remains partially idle. 

AWS notes that when engineering licenses reach full utilization, jobs can be delayed or fail if scheduling does not account for license availability. This can result in wasted compute resources and longer user wait times.

How Unused and Misallocated Licenses Quietly Drain Budgets 

License constraints can waste compute capacity when scheduling decisions account for CPUs, GPUs, and memory but ignore software availability. A job may have suitable compute resources available and still be unable to run because its required application or feature license is already checked out. 

The resulting costs are operational as well as financial:

• Longer job queues: Engineers wait for licenses even when suitable compute capacity is available. This extends simulation, verification, and other engineering turnaround times.

• Underused infrastructure: Compute resources assigned to blocked or failed jobs do not contribute to productive work. AWS mentions that EDA environments can have jobs waiting for either a license or a suitable compute node, making both resources important to overall efficiency.

• Poor capacity decisions: If license shortages are not visible, teams may interpret persistent job delays as a compute problem and request additional nodes. More compute will not resolve a license bottleneck.

• Higher operational overhead: Administrators have to determine whether a delay comes from compute capacity, scheduling, or a license constraint before they can address it.

Financial losses can be substantial. A 2024 report cited by the American Society of Mechanical Engineers (ASME) found that 55% of large-enterprise software licenses go unused. This means an average of $127.3 million in wasted annual spend. The figure covers enterprise software broadly, not HPC licenses specifically, but it shows the scale of waste that can result when organizations lack visibility into software usage.

License-aware scheduling addresses this problem by treating a software license as a consumable resource, alongside CPU, GPU, memory, and other cluster resources. The scheduler can then hold a job until all required resources are available, rather than allocating compute that the job cannot use. 

At the planning level, teams should compare license demand with compute utilization to determine which resource is limiting throughput. This prevents organizations from adding expensive infrastructure when better license allocation or additional software capacity is the real requirement.

How Regional Sharing Improves License Utilization

Separate license pools can create an artificial shortage. A team in one region may be waiting for a license while the same license sits unused at another site. This becomes especially relevant in global engineering environments where demand shifts across time zones.

For example, an engineering team in North America may reach peak demand for an EDA tool during its working hours, while a European or Asian team has already finished its workday. If each region has a dedicated license pool, unused capacity in one location cannot help the team facing a shortage.

Where licensing agreements permit it, organizations can instead treat licenses as a shared portfolio of capacity.

The benefit is not simply higher utilization. Regional sharing can also reduce unnecessary license purchases by showing whether an apparent shortage is actually a distribution problem.

Effective sharing requires visibility into:

• Current usage: Which licenses are checked out and where?

• Available capacity: Which licenses could be used elsewhere?

• Demand patterns: When does each region reach peak usage?

• Allocation policies: Which teams or projects have priority?

• Historical demand: Is a shortage persistent or limited to specific periods?

Siemens’ HPCWorks™  Monitor software supports multi-site license management and can monitor remote instances to enable license sharing across geographic locations. It tracks license usage in real time across more than 16 license manager applications, with reporting by users, projects, and business units.

For organizations operating across multiple sites, this changes the procurement question. Instead of asking “How many licenses does each region need?” teams can ask “How much license capacity does the organization need, when is it needed, and where can existing capacity be shared?” That shift helps organizations make better use of licenses they already own before committing to additional seats.

How License Data Improves Infrastructure and Procurement Decisions

License data is most useful when analyzed alongside compute and workload data. Information about CPU and GPU utilization can indicate whether infrastructure is available, but it does not indicate whether the software needed to use that infrastructure is available.

A simple comparison helps identify the actual constraint:

This visibility prevents teams from solving the wrong problem. If jobs regularly wait for a solver license while CPU capacity remains available, adding more nodes will not increase throughput. If licenses are available but the cluster is consistently saturated, purchasing more software seats will not resolve the compute bottleneck.

Historical license data is also important for procurement. Current utilization shows what is happening now; historical data shows whether that pattern is persistent. Teams can use it to identify:

• Peak concurrent usage

• Recurring periods of license contention

• Consistently underused licenses

• Demand by region, department, or application

• Changes in utilization before renewal

• Capacity that could be shared instead of purchased

HPCWorks Monitor provides real-time and historical license data, including hourly heatmaps that show license activity across a 24/7 workweek. This can help identify peak usage and periods of resource congestion.

For broader enterprise license management, HPCWorks Insight Pro™ License Analytics software provides real-time reporting, historical analysis, organizational breakdowns, and future license-utilization forecasting. It enables organizations to optimize software portfolios and make informed decisions on purchases and license sharing across multiple sites.

The result is a smarter, evidence-based planning process. Instead of automatically renewing every license or adding capacity when users report shortages, teams can determine whether the right response is to renew, reduce, reallocate, share, or purchase.

A Practical Framework for Improving License Utilization 

Improving license utilization does not require an immediate increase in software capacity. Start by understanding how existing licenses are consumed, then align that capacity with actual workload demand.

1. Establish visibility: Track licenses owned, active checkouts, available capacity, peak usage, demand, and regional allocation.

1. Correlate licenses with jobs: Map applications and workloads to their required licenses. Identify jobs that are delayed because a required license is unavailable.

1. Reclaim and reallocate: Identify inactive or consistently underused licenses. Reassign available capacity to teams or projects with active demand.

1. Introduce sharing: Where licensing terms permit, create shared pools across sites, regions, or business units. This can reduce idle capacity in one location while another faces a shortage.

1. Make scheduling license-aware: Include license availability when deciding whether a job can run. Avoid reserving compute resources for workloads that cannot obtain the required software licenses.

1. Forecast before renewal: Use historical and peak-demand data to decide whether to renew, reduce, reallocate, share, or purchase additional capacity.

The objective is not to maximize license utilization at any cost. It is to align software capacity with workload demand so that both licenses and compute resources are used productively.

Conclusion 

HPC efficiency depends on more than CPU, GPU, storage, and cluster utilization. A workload can have sufficient compute capacity and still be unable to run because a required software license is unavailable.

Treating licenses as operational resources gives engineering teams a clearer view of where capacity is being consumed, where it is sitting idle, and what factors are limiting throughput. That visibility enables better scheduling, regional sharing, license allocation, renewals, and infrastructure planning – and it can ultimately improve productivity and your bottom line.

If license waste, regional shortages, or poor visibility are limiting your HPC efficiency, contact Siemens to optimize license utilization and capacity planning.