The Parallel Computing Capability Overview
STK's Parallel Computing capability enables STK to distribute many of its most computationally complex analysis tasks across multiple computing cores on the computer where it is installed.
Parallel Computing also includes software development kits (SDK) for .NET, Java, and Python. These SDKs make it easy to parallelize the execution of custom models and algorithms.
- Chain Access
- Coverage and Figure of Merit
- Deck Access
- Record a Movie
- Volumetric
- STK Analyzer. See the STK Analyzer Help for more information.
In the future, more computations will be supported.
The parallel computing capability can be used either with multiple cores on a single standalone machine or with multiple machines interconnected in a cluster.
Licensing
You will need an Ansys HPC license and one of the following licenses:
| Capability | Pro license | Premium (Air/Space) and Enterprise |
|---|---|---|
| Parallel (local) | 8 cores | 16 cores |
| Parallel (cluster) | Uses the HPC license to determine the number of cores used. | |
An STK user can only use one license at a time, either local or cluster.
Computing with multiple machines in a cluster requires a Parallel Computing server. For more information, see the separate
The following paragraphs detail each configuration.
Standalone computer
In the case of a standalone computer, Parallel Computing will utilize the cores available on the local machine to perform the computation. The extension is installed automatically with the regular STK install.
While it is not strictly required to understand how everything works in order to use the parallel capabilities, here are a few highlights that might facilitate troubleshooting. Basically, as shown in Figure 1, Parallel Computing exports the current scenario to a temporary VDF, and then spawns worker processes to parallelize the computations. By default, it uses one worker per core. Each worker performs a part of the computation, and then the results are sent back to the STK desktop application through shared memory. Then the STK desktop application combines the results together.
Figure 1: Standalone machine configuration.
The advantage of this configuration is that it is straight forward to set up, as everything is local to one machine. It is also included with STK and works out of the box with a standard STK installation. However, this configuration only allows you to scale vertically (i.e., to improve performance you need to add more resources - CPU, RAM - to your local machine). In the case of big STK coverage, deck access, chain access, or volumetric computations, it might also be advantageous to scale horizontally (i.e., add more machines to share the work), which can be achieved by using a cluster of machines.
Cluster of Machines
In the cluster configuration, there are several machines involved with different roles:
- Clients running STK desktop submit tasks to a centralized coordinator.
- The Coordinator receives the work requests from the client(s) and distributes the tasks to agents.
- The Agents execute the tasks. Only one agent runs per machine in the cluster. By default the number of hosts running in parallel on a machine is set to the number of logical cores available on that machine. However this default setting can be overwritten when configuring the agents.
- The coordinator and the agent tray applications provide a way to configure and monitor the activity of the coordinator and of the agent(s). The coordinator and the agents run as Windows services.
Figure 2: Cluster configuration.
Figure 2 shows how the clients, coordinator and agents interact. Notice that these three programs could be running on the same machine, or could also be running on three separate machines. The agents spawn worker processes that run on the same machine as the agent itself.
Figure 3: Example of a cluster.
Figure 3 shows an example where two clients running STK use a two machine cluster to perform computations: the first machine running both the coordinator and the agent, and the second machine running only the agent. You can scale horizontally in this configuration simply by adding more machines to the cluster.
For more details about managing the cluster, see the separate