avi, and also tune your settings by adjusting the framerates for example. You can create numerous animation types, such as. Once the images were created, we recommend using tools such as ImageMagick or FFmpeg (executable shipped with Tecplot 360) where you can stitch together the exported images. With parallel processes you can get hardware contention (which is why we don’t see a linear improvement in performance), so it may take some experimentation with your data to find the ideal amount of concurrency to use. We observed nearly identical speed with half the RAM when parallelizing with 8 processors vs. Our script, running with 16 cores, took an average of ~1.2 seconds per image-this is 4.8 times faster than Tecplot 360’s fastest run without parallelizing the image exports and 10.7 times faster than ParaView! However, this comes at a significant cost in terms of RAM (34.7 vs 2.74 Gb, 12.6 times more RAM). In the plot below you can see how running even just two concurrent processes can drastically reduce the processing time:įigure 4: Total time to Execute and RAM Usage against # of Parallel 360 Processes We utilized our ParallelImageCreator.py PyTecplot script (located on our GitHub - see the documentation for examples) which processes multiple timesteps simultaneously to put more cores to work. batch mode) is that image export scripts can be written to use multiple concurrent PyTecplot processes-slashing the time to process images. Parallel ExecutionĪnother benefit of PyTecplot (i.e. As such, we did not run ParaView using MPI. Furthermore, the ParaView CONVERGE reader does not read in parallel. CONVERGE data is single block, so it’s not ideal for distribution. Setting Tecplot 360 to minimize memory mode prevents it from saving data from previous time steps in RAM.ĭid we run ParaView using MPI? – The current recommendation is to only run ParaView using MPI when you can distribute the data. This behavior is handy in the GUI when moving back and forth between time-steps. Once the RAM meets the 70% threshold, Tecplot 360 will offload stored memory until it reaches 30%. Note, Tecplot 360 defaults to keeping 30-70% of the data that it’s loaded in RAM in the event that this data may need to be re-rendered. Tecplot 360 in minimized memory mode also held the least amount of RAM at 2.7 Gb peak RAM usage.
TECPLOT DOWNLOAD SERIAL
So, serial here means that we loaded the series of CONVERGE post*.h5 files and ran through the animation, creating an image one timestep at a time.įor the HDF5 dataset performance tests, we discovered that processing with Tecplot 360 in minimized memory mode gave the fastest processing time at 1.99 hours, ~5.6 seconds per image. ParaView does not publish a list of which filters are multi-threaded, but “all common filters are ” Figure 1 shows an example of the plot:įigure 3: 360 vs ParaView: Execution Time & Peak RAM Usageįor the generated plot, Tecplot 360 uses multi-threading for a number of operations such as: deriving node-located values from the cell-centered Temperature value (a pre-requisite for iso-surface creation), slice creation, and iso-surface creation. We repeated this for each time step in the data series for a total of 1278 images produced. The goal is to capture the execution time and peak RAM required to create a plot which consists of a slice colored by temperature and an iso-surface of the flame front (temperature = 1700). This dataset is composed of 1278 timesteps, totaling 169Gb on disk. Since many CONVERGE users need to create movies of their results, we chose a reasonably large, multi-cycle internal combustion engine (ICE) simulation. By avoiding running post_convert you’re saving yourself time and disk space. TfC, 360, and ParaView each include a direct data reader for CONVERGE post*.h5 files, which means that with these post-processors there’s no need to run post_convert. batch-processing), the most popular options are Tecplot 360 or ParaView. However, for users who are looking for more power than what TfC offers out of the box (i.e. For today’s post we’ll be diving into performance with CONVERGE data only.ĬONVERGE users are fortunate to have access to ‘Tecplot for CONVERGE’ (TfC) as part of their CONVERGE license. Of course, performance is dependent on several factors – and which data type you are using is an important one. ParaView for CONVERGE DataĪt Tecplot we know that you have choices when it comes to post-processing and that your time is important to you, so we’ve done some performance testing to help you decide which post-processor will perform the best with your data. « Back Which is Faster? CONVERGE, Tecplot 360, Tecplot Blog September 16, 2022