Effective Use of MPI to Speed Solutions

The next release of FDS and PyroSim will make changes that make it easier to run a large number of meshes without the need for the user to assign each mesh. See the discussion of Case #8445 FDS 6.7.4 significantly slower than FDS 6.7.0 when processes "over-subscribed" in the FDS Issue Tracker (https://github.com/firemodels/fds/issues/8445). This post will be updated when these changes are made. 

The MPI parallel version of FDS can be used with multiple meshes to significantly reduce PyroSim/FDS solution times. Starting with PyroSim 2019.1 changes in the behavior of MPI multi-process execution require users to manually assign meshes to processors when the number of meshes exceed the number of logical processors. This post will provide some background and examples of how to effectively use MPI to speed solutions.

Prior to PyroSim 2019.1, the user could simply click Run FDS Parallel and PyroSim would launch a process for each mesh. If the number of meshes (processes) exceeded the number of logical processors, the FDS MPI implementation would “round-robin” through all the processes, automatically balancing the processor load. FDS 6.7.1 that was included in PyroSim 2019.1 changed to a new FDS MPI implementation. Now, run times become significantly longer if more processes are started than the number of available logical processors. As a result, the maximum number of processes started by PyroSim is now limited to the number of logical processors. This can cause long run times if the user clicks Run FDS Parallel and the number of meshes exceeds the number of processors, since all additional meshes are assigned to the last processor. 

In this post, we will focus on running MPI on a single Windows computer with a multi-core processor. We will focus on a simple approach, but the user should recognize that there may be different options that could additionally speed the solution for your case.

The FDS User Guide has a good background description on the use of multiple processors in Chapter 3, Running FDS. In the following description, sentences in quotes are taken from the FDS User Guide.

Cores and Logical Processors

You will need to know the number of cores and logical processors of your computer. “For a typical Windows computer, there is one CPU per socket. Each CPU typically has multiple cores, and each core is essentially an independent processing unit that shares access to power and memory.” Processors manufactured by Intel support a technology named Hyper Threading that doubles the number of apparent processors. These processors are called logical processors to distinguish them from the physical cores. To effectively run FDS, you must not launch more processes than the number of available logical processors.   Be aware that depending on computer configuration, the number of logical cores may not be double the physical core count, even if the processor is capable of hyper threading.

To find the number of sockets, cores, and logical processors in Windows start Task Manager and click the Performance tab. The values for your computer will be displayed in the CPU section, as shown in Figure 1. (As an aside, to change the Task Manager graph from showing overall CPU utilization to individual processors, right-click on the graph, select Change graph to, and click Logical processors.)

Figure 1: Task Manager information showing the number of Sockets (CPUs), Cores, and Logical processors.

Note that the number of processors depends on your computer, while the number of processes is defined at run time and indicates how many of the available processors will be used during the solution. 

Strategies for Using MPI

To use MPI for a parallel solution you must have multiple meshes. There can be many constraints that dictate how the meshes are arranged. If modeling a fire, meshes with smaller cells will typically be used for the fire and meshes with larger cells will be used away from the fire. Geometry, such long halls or atria can  impose additional constraints. Accurate modeling of fans and HVAC flow may require a fine mesh resolution (see Two-dimensional benchmark test).

Within these constraints, we will describe two basic approaches that should result in near-optimal solution times. We will then provide examples of each.

Simplest Approach: The number of meshes is less than or equal to the number of logical processors

For the simplest approach, use the following guidelines:

  1. If possible, mesh the problem so that the number of meshes is equal to the number of cores on your computer. If that is not possible, you must keep the number of meshes less than or equal to the number of logical processors.
  2. All meshes should have approximately the same number of cells. 

To run the simulation, on the Analysis menu click Run FDS Parallel. Now wait. Get a cup of coffee, or maybe eat dinner, or maybe go to bed and check progress tomorrow! See the hints on running long problems below.

Second Approach: The number of meshes exceeds the number of logical processors

If the number of meshes exceeds the number of available logical processors, each mesh must be manually assigned to a process by the user. The Run FDS Cluster option is then used for the solution. 

At this time, assigning meshes to processes is a manual operation and is described in the post Assign FDS Meshes to Specific MPI Processes. Use the following guidelines:

  1. Mesh the problem as required for your solution. 
  2. Decide how many processes you will use. A good place to start is to use the number of cores on your computer.
  3. Assign meshes to each process.
  4. Assign the meshes so that the total number of cells assigned to each process is approximately the same. Note that numbering of processes starts at zero, so eight processes will be numbered 0 through 7.
  5. Run the simulation using the Run FDS Cluster option which will open the Cluster FDS Parameters dialog. Specify the number of processes. If you used eight processes when assigning the meshes in Step 3, specify 8 in this dialog.
  6. To run the simulation, click OK in the Cluster FDS Parameters dialog. You might need to give your computer password. Separately you can follow these instructions to set the password for future use. See the hints on running long problems below.

Step 3 is illustrated in Figure 2 and Figure 3 which show a simulation with 32 meshes using 8 processes. In this case, meshes 1 through 4 are assigned to MPI_PROCESS 0 (Figure 2), meshes 5 through 8 are assigned to MPI_PROCESS 1, continuing until mesh 32 is assigned to MPI_PROCESS 7 (Figure 3). The number of threads is specified as one (N_THREADS 1) for all meshes.

Step 4 is illustrated in Figure 4 which shows the Run FDS Cluster dialog and how to assign 8 processes.

Figure 2: Assign Mesh01 to process 0.
Figure 3: Assign Mesh32 to process 7.
Figure 4: Run FDS Cluster.

How to Verify Processor Load Balance

“At the end of a calculation, FDS prints out a file called CHID_cpu.csv that records the amount of CPU time that each MPI process spends in the major routines. For example, the column header VELO stands for all the subroutines related to computing the flow velocity; MASS stands for all the subroutines related to computing the species mass fractions and density.”

Figure 5 shows a case where some meshes were not assigned to processes. As a result, the last process was assigned all remaining meshes.  This makes the total run time longer as most processes must wait on the overloaded process as the simulation steps through time.

Figure 6 shows a case where the number of meshes and processes were the same, so the load is balanced. 

Figure 5: The CHID_cpu.csv file for a case that used 16 processes and had 32 unassigned meshes. The last process had to solve the last 16 meshes.
Figure 6: The CHID_cpu.csv file for a case that used 8 processes and had 8 unassigned meshes. The load is balanced for all processes.


Solution Speed Using MPI

The model shown in Figure 7 was used to test the effect of using different numbers of meshes and MPI processes. The geometry remained the same, but cases were run with 1 to 32 meshes. Cases were run with 0.8M, 1.6M, and 6.4M total cells in the model. There was no specified flow or fire in the model. The top boundary was an OPEN surface.

Using more cells can rapidly increase the solution time. For example, if you have a model with 1M cells, reducing the size of the cells by 1/2 in the X, Y, and Z directions will give you 8M cells. In addition, the time step will be reduced by a factor of two. So, simply refining the mesh by a factor of two will result in approximately a factor of 16 increase in run time. This is why you only want to refine the mesh where necessary.

Figure 7: Simple model used to test 1 to 32 meshes with 0.8M to 6.4M cells. No fire or obstructions. The top boundary is an OPEN surface. The solution end time was 10 seconds.

The solution times are shown in Figure 8. As noted, the computer had 8 cores with 16 logical processors. For meshes 1 through 16 the number of processors is the same as the number of meshes and the solution was simply run by clicking Run FDS Parallel. For the 32 mesh case, 2 meshes were assigned to each of the 16 processes and the solution used Run FDS Cluster.

The test computer has 8 cores. For the test problem, the shortest solution times are for the cases where the number of MPI processes matched the number of cores (8 meshes and 8 cores). It is not guaranteed that matching the number of MPI processes and cores will always result in shortest times, but this provides guidance that should give reasonable results.

You can also see from the results that there are diminishing returns as you add additional meshes and associated processes.  If you have a server computer which may have as many as 32 physical cores or more, you may use your compute resources by running multiple models or cases simultaneously.  Since the speed gained by moving from 4 to 8 meshes or from 8 to 16 may not be significant, you may get more total work finished faster by running 4 simultaneous 4-mesh problems using the same 16 cores instead of running each case with 16 meshes in sequence.

Figure 8: Solution times as a function of meshes. The computer had 16 logical processors. For meshes 1 through 16 the number of processors is the same as the number of meshes. For the 32 mesh case, 2 meshes were assigned to each process.
Figure 9: Details of solution times.

A Few Variations

We will briefly discuss three variations for this test problem. 

  1. The first case is 0.8 M cells, 16 meshes, and 8 processes with 2 threads for each process. This gave a run time of 66.12 seconds, slightly slower than the best time of 58.37 seconds.
  2. The second case is 0.8 M cells, 32 meshes, and 8 processes with 4 meshes assigned to each process in a linear pattern (Figure 10). This gave a run time of 78.05 seconds, again slower than the best time of 58.37 seconds.
  3. The third case is 0.8 M cells, 32 meshes, and 8 processes with 4 meshes assigned to each process in a section pattern (Figure 10). This gave a run time of 80.04 seconds, slower than the best time of 58.37 seconds.

The apparent conclusion, at least for this test problem, is that matching cores and meshes gives the shortest time.

Figure 10: Mesh patterns

Fire Simulation

A simple fire simulation illustrates using refined cells near the fire and larger cells away from the fire. This model used 8 meshes. The fire had a heat release rate of 2 MW. For a 2 MW fire, D*/10 = 0.126536 m so the cell size of the meshes around the fire is 12.5 cm. Away from the fire a cell size of 25.0 cm is used. 1.5 M cells were used with all meshes having an equal number of 200,000 cells, except for Mesh01 that had 100,000.

Figure 11: Simple fire model that used different cell sizes.

The solution was run for 100 seconds. A visual check of the results shows a smooth transition across the boundaries of meshes with small cells and larger cells. 

Figure 12: Smoke in simple fire model. Note that minimal changes are noted across mesh boundaries with different cell sizes.

Topics Not Addressed in this Post


“If your simulation involves only one mesh, you can exploit its multiple processors or cores using OpenMP.” Figure 14 shows the default PyroSim OpenMP settings that use up to four OpenMP threads when running with one processor (Run FDS) and one OpenMP thread when running with multiple processors (Run FDS Parallel). We do not recommend that the user change these values.

Figure 14: PyroSim settings for OpenMP

The FDS User Guide states “Because it more efficiently divides the computational work, MPI is the better choice for multiple mesh simulations. … It is usually better to divide the computational domain into more meshes and set the number of OpenMP threads to 1.” This matches our experience.

For more information see the post OpenMP Benchmarks and the FDS User Guide.

Using a Large Number of Processors

This post applies to problems with a moderate number of meshes (less than 50). See the FDS User Guide for running thousands of meshes.

Running Long Problems

If a simulation is going to take more than one day, first make sure that you really need that resolution. Before starting the final analysis, run a short test case to make sure everything is correct.

It is useful to run a long problem by breaking it into segments, using restarts for each segment. While it is theoretically possible to run a long simulation saving restarts at intervals while continuing the solution, our experience favors running the solution for a time segment, stopping the solution at the end of that segment, and saving a restart at that time. This gives a reliable restart and the continuing solution will be correctly appended to the existing outputs. If your computer stops prematurely you can restart from the last saved location.

Here are the steps:

  1. Based on your short run, estimate the solution time that will be reached in 24 hours (or whatever time segment you want to use). Specify this as the end time (for example 100 s) and specify the restart interval to match that time (100 s). In the PyroSim Analysis menu, click Run FDS Parallel or Run FDS Cluster as appropriate.
  2. Run the first segment. Carefully review the results. To be extra careful, copy the entire simulation folder (with results included) to a new folder. You can rename the new folder, but do not change the model name.
  3. To run the next segment, open the model in the new folder and specify the end time equal to the previous end time (100 s) + the time for this segment (for example 150 s). In this case the end time would be 250 s. Specify the restart interval to match the segment time (150 s). In the PyroSim Analysis menu, click Resume FDS Parallel or Resume FDS Cluster as appropriate.
  4. Repeat step 3 until the solution is finished.


Thank you to Kevin McGrattan, NIST, for clarifying communications about MPI. Also, discussions with Bryan Klein were helpful.

Input Files

Download the input files here.

MPI Input Files

History of Edits to this Post

2020-06-16 – Edited second paragraph to more clearly identify the problem when more processes are started than available logical processors. 

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