Simulation
Definition
Using powerful cloud computers to create virtual models of real-world systems and test how they behave under different conditions.
Use Cases
- BMW Group: Running large-scale engineering simulations (e.g., aerodynamics/CFD and vehicle development) to shorten design cycles — Uses HPC resources on AWS to run simulation workloads at scale, leveraging cloud elasticity to add capacity when needed rather than relying only on fixed on-prem clusters (Faster access to compute capacity for simulation peaks and improved time-to-results for engineering teams by scaling resources on demand)
- Siemens: Providing cloud-based engineering simulation for product design and digital twin scenarios — Offers simulation capabilities delivered on public cloud infrastructure (including AWS) so customers can run compute-intensive CAE workloads without owning HPC hardware (Enables customers to run larger or more frequent simulations with less upfront infrastructure investment and faster iteration during product development)
- Netflix: Simulating failure scenarios (chaos engineering) to validate system resilience under adverse conditions — Runs controlled experiments in production-like environments on AWS using internal tooling (e.g., Chaos Monkey and related practices) to simulate instance and service failures (Improved reliability by proactively finding weaknesses before real outages occur, increasing confidence in system behavior under failure conditions)
Provider Equivalents
- AWS: AWS ParallelCluster
- Azure: Azure CycleCloud
- GCP: Google Cloud Batch
- OCI: OCI HPC Cluster
Frequently Asked Questions
- What's the difference between Simulation and Emulation?
- Simulation models how a system behaves using mathematical or logical models (e.g., airflow over a car body). Emulation tries to reproduce the exact behavior of a specific real system or hardware/software environment (e.g., running software for one CPU architecture on another). Simulation is common in engineering and science; emulation is common in compatibility testing and legacy systems.
- When should I use Simulation?
- Use simulation when real-world testing is too expensive, slow, dangerous, or impossible. Common triggers include: you need to test thousands of scenarios (parameter sweeps), you want to optimize a design before building prototypes, you need to model rare events (extreme weather, failures), or you need repeatable experiments. Cloud simulation is especially useful when you have bursty demand and want to scale compute for short periods.
- How much does Simulation cost?
- Costs depend mainly on compute time (CPU/GPU hours), memory needs, storage for input/output datasets, and data transfer. HPC simulations often cost more per hour due to high-end instances (GPUs, high-frequency CPUs, fast networking), but can be cheaper overall if they finish much faster or replace physical prototypes. To control costs, use right-sized instances, spot/preemptible capacity where appropriate, autoscaling, and job scheduling to avoid idle clusters.
Category: software
Difficulty: intermediate
Related Terms
See Also