32.1 Introduction to Parallel Processing

The ANSYS FLUENT serial solver manages file input and output, data storage, and flow field calculations using a single solver process on a single computer (Figure  32.1.1).

Figure 32.1.1: Serial ANSYS FLUENT Architecture

ANSYS FLUENT's parallel solver allows you to compute a solution by using multiple processes that may be executing on the same computer, or on different computers in a network (Figure  32.1.2).

Figure 32.1.2: Parallel ANSYS FLUENT Architecture

Parallel processing in ANSYS FLUENT involves an interaction between ANSYS FLUENT, a host process, and a set of compute-node processes. ANSYS FLUENT interacts with the host process and the collection of compute nodes using a utility called cortex that manages ANSYS FLUENT's user interface and basic graphical functions.

Parallel ANSYS FLUENT splits up the mesh and data into multiple partitions, then assigns each mesh partition to a different compute process (or node). The number of partitions is an integral multiple of the number of compute nodes available to you (e.g., 8 partitions for 1, 2, 4, or 8 compute nodes). The compute-node processes can be executed on a massively-parallel computer, a multiple-CPU workstation, or a network cluster of computers.

In general, as the number of compute nodes increases, turnaround time for the solution will decrease. However, parallel efficiency decreases as the ratio of communication to computation increases, so you should be careful to choose a large enough problem for the parallel machine.

ANSYS FLUENT uses a host process that does not contain any mesh data. Instead, the host process only interprets commands from ANSYS FLUENT's graphics-related interface, cortex.

The host distributes those commands to the other compute nodes via a socket interconnect to a single designated compute node called compute-node-0. This specialized compute node distributes the host commands to the other compute nodes. Each compute node simultaneously executes the same program on its own data set. Communication from the compute nodes to the host is possible only through compute-node-0 and only when all compute nodes have synchronized with each other.

Each compute node is virtually connected to every other compute node, and relies on inter-process communication to perform such functions as sending and receiving arrays, synchronizing, and performing global operations (such as summations over all cells). Inter-process communication is managed by a message-passing library. For example, the message-passing library could be a vendor implementation of the Message Passing Interface (MPI) standard, as depicted in Figure  32.1.2.

All of the parallel ANSYS FLUENT processes (as well as the serial process) are identified by a unique integer ID. The host collects messages from compute-node-0 and performs operations (such as printing, displaying messages, and writing to a file) on all of the data, in the same way as the serial solver. You have the option of bypassing the host when inputting or outputting parallel data files, so that the files are passed directly between the compute nodes and the disk in a parallel fashion. This can reduce the time for data file I/O operations (see Section  4.4 for details).

Recommended Usage of Parallel ANSYS FLUENT

The recommended procedure for using parallel ANSYS FLUENT is as follows:

1.   Start up the parallel solver. See Section  32.3 and Section  32.4 for details.

2.   Read your case file and have ANSYS FLUENT partition the mesh automatically upon loading it. It is best to partition after the problem is set up, since partitioning has some model dependencies (e.g., adaption on non-conformal interfaces, sliding-mesh and shell-conduction encapsulation).

Note that there are other approaches for partitioning, including manual partitioning in either the serial or the parallel solver. See Section  32.5 for details.

3.   Review the partitions and perform partitioning again, if necessary.
See Section  32.5.7 for details on checking your partitions.

4.   Calculate a solution. See Section  32.7 for information on checking and improving the parallel performance.