Simulation data sets have a funny habit of ballooning as engineers move through the development cycle. At some point, post-processing these data sets on a single machine becomes impractical.
Engineers can speed up post-processing by spatially or temporally decomposing large data sets so they can be post-processed across numerous servers.
The idea is to utilize the idle compute nodes you used to run the solver in parallel to now run the post-processing in parallel.
In ANSYS 19.2 Ensight Enterprise you can spatially or temporally decompose data sets. Ensignt Enterprise is an updated version of EnSight HPC.
Post-Processing Using Spatial Decomposition
EnSight is a client/server architecture. The client program takes care of the graphical user interface (GUI) and rendering operations, while the server program loads the data, creates parts, extracts features and calculates results.
If your model is too large to post-process on a single machine, you can utilize the spatial decomposed parallel operation to assign each spatial partition to its own EnSight Server. A good server-to-model ratio is one server for every 50 million elements.
Each EnSight Server can be located on a separate compute node on any compute resource you’d like. This allows engineers to utilize the memory and processing power of heterogeneous high-performance computing (HPC) resources for data set post-processing.
The engineers effectively split the large data set up into pieces with each piece assigned to its own compute resource. This dramatically increases the data set sizes you can load and process.
Once you have loaded the model into EnSight Enterprise, there are no additional changes to your workflow, experience or operations.
Post-Processing Using Temporal Decomposition
Keep in mind that this decomposition concept can also be applied to transient data sets. In this case, the dataset is split up temporally rather than spatially. In this scenario, each server receives its own set of time steps.
EnSight Enterprise offers performance gains when the server operations outweigh the communication and rendering time of each time step. Since it’s hard to predict network communication or rendering workloads, you can’t easily create a guiding principle for the server-to-model ratio.
However, you might want to use a few servers when your model has more than 10 million elements and over a hundred time steps. This will help keep the processing load of each server to a moderate level.
How EnSight Speeds Up the Post-Processing of Large Simulation Data Sets
Another good tip to ensure you are post-processed optimally within EnSight Enterprise. Engineers achieve the best performance gains by pre-decomposing the data and locating it locally to the compute resources they anticipate using. Ideally, this data should be in EnSight Case format.
To learn more, check out Ensight or register for the webinar Analyze, Visualize and Communicate Your Simulation Data with ANSYS EnSight.