Speed Up SIMULIA Abaqus:
Practical Tips to Reduce Run Time

Joseph Y. OZTURK

Joseph holds a B.Sc. in Electrical and Electronics Engineering and is supported by a broad set of Dassault Systèmes brand essentials & industry fundamentals certifications. He supports the end to end rollout and licensing lifecycle of simulation software, including coordinating orders, renewals, and subscription administration, assisting customers with installation and environment setup, and managing technical requests with engineering specialists to ensure clear and timely resolution. He also develops and maintains the company website from a technical perspective, focusing on site structure, performance, and well organized technical content that supports engineering services and software solutions.

Speed Up SIMULIA Abaqus: Practical Tips to Reduce Run Time

Maybe you recognize this: the job starts, the monitor window opens, and nothing seems to happen. The run time is longer than expected and the first question is always the same. How can this be faster without compromising the reliability of the results?

Most Abaqus slowdowns come from a small set of causes: overly dense meshes, excessive output, heavy contact, memory and scratch bottlenecks, or CPU settings that do not scale well. The tips below focus on the changes that typically produce the biggest improvements.

In this article

Reduce Abaqus run time without blindly reducing accuracy
Avoid setup choices that quietly slow jobs down
Improve hardware usage with smarter job settings

1) Start by deciding what actually needs high detail

Abaqus can only solve what is modeled. If everything is modeled in maximum detail, run time increases quickly.

Practical ways to reduce unnecessary work:

Use symmetry when the physics allow it
Simplify tiny features that do not affect the result of interest
Refine locally where stresses, contact, or gradients matter
Consider submodeling when only a small region needs fine detail

This approach often improves both speed and clarity because it forces a clear definition of what the analysis is trying to answer.

2) Mesh smarter, not denser

More elements usually means more time, but a denser mesh does not automatically produce a better answer.

Mesh habits that help speed while maintaining reliability:

Refine only in critical zones such as hot spots and contact areas
Keep element quality high since poor quality can slow convergence
Prefer structured meshes where possible
Avoid extreme size jumps between neighboring elements
Perform mesh sensitivity analysis to confirm results remain consistent

Targeted meshing is usually the best combination of speed and accuracy.

3) Control output requests before pressing Run

Output is one of the most common hidden performance killers. Writing large field outputs too frequently can add significant time, especially for long nonlinear runs.

Quick improvements:

Reduce field output frequency for large models
Request only variables that will be used in review and reporting
Use history output for a small set of key points or sets
Avoid generating large print files unless needed

A simple rule is to record what will be used to make decisions, not everything that is available.

4) Simplify contact and constraints where possible

Contact is powerful, but it can be expensive. Constraints can also add overhead, and some choices are heavier than they look.

Common improvements:

Keep contact definitions as simple as possible while still correct
Reduce contact pairs and contact scope where appropriate
Avoid tying large regions unless it is truly required
Review MPC, coupling, tie constraints, and connectors for size and purpose
Avoid over constraining the model since it can slow the run and reduce realism

Small adjustments here can improve performance and solution robustness at the same time.

5) Choose the right approach: Abaqus/Standard vs Abaqus/Explicit

Solver choice can change run time dramatically. Standard and Explicit are designed for different problem types, and choosing the wrong approach can add hours or days.

General guidance:

Abaqus/Standard is often effective for many quasi static problems when convergence is expected
Abaqus/Explicit is often preferred for severe contact, complex events, or highly nonlinear behavior that is difficult to converge in Standard

For quasi-static analyses in Explicit, methods such as load rate selection and mass scaling can reduce run time, but they must be used responsibly. Always verify that the physics remain valid, for example by monitoring energy balance and confirming that dynamic effects are not dominating the response.

6) Optimize memory and scratch so the job is not limited by I/O

Abaqus can slow down significantly when the job relies heavily on disk I/O instead of memory.

Helpful practices:

Ensure sufficient RAM is available for the job
Use fast local storage such as SSD for scratch when possible
Keep scratch location on a stable, high performance drive
Close heavy background applications before long runs

If the system is constantly reading and writing large scratch files, run time will increase.

7) Use CPUs effectively and test scaling rather than guessing

Parallelization can reduce run time, but only if the model allows the problem to scale well.

Practical tips:

Use physical cores as the baseline since virtual cores do not always help
Test a short portion of the run with different CPU counts to find the best value
If scaling is poor, increasing CPUs may not reduce time and can waste resources
Heavy contact and large constraint regions can limit scaling benefits

The best setting is the one that gives the best time reduction per resource used, not always the maximum available.

8) Run a short pilot job before committing to the full run

A short pilot run can prevent hours of wasted time.

Use a pilot to confirm:

contact behavior looks correct
boundary conditions are not over constraining the model
output is not excessive
the job runs smoothly and CPU settings scale as expected

This is especially useful for large nonlinear analyses, where early mistakes are expensive.

9) Keep the workstation environment stable for long runs

This is not a solver feature, but it helps in practice:

restart the system before very long analyses when it has been running for many days
pause heavy downloads or cloud sync during long jobs
keep resource heavy applications closed during solver runs

The goal is to give Abaqus a stable environment with predictable resources.

Quick checklist: fastest improvements to try first

Reduce field output frequency and variables
Use symmetry, simplify geometry, or apply submodeling where appropriate
Refine the mesh only where it matters and improve element quality
Review contact definitions and large constraints
Confirm scratch location and storage performance
Test CPU count to find the best scaling point

Need help improving Abaqus run time?

For teams using SIMULIA Abaqus, ENA2 supports licensing and setup, installation and environment configuration guidance, and responsive technical support when issues arise. Certified training is also available to help users build efficient modeling habits and reduce preventable slowdowns. If the goal is to improve Abaqus run time or streamline workflows, contact ENA2 to discuss the best next steps.

Speed Up SIMULIA Abaqus:
Practical Tips to Reduce Run Time

Joseph Y. OZTURK