#Paraview parallel software#

Efficiency may dictate writing special filters to handle the multiblock anyway. The way the pipeline iterates over multiblocks is proving to be not the fastest way to handle the multiblock data sets, especially when there are many blocks.

That does not exactly guarantee efficiency.

Only works for multiblocks and at whatever granularity the blocks in the data are stored.

We would like to tell the executive to not re-initialize the output for filters which can re-use the last result. Currently, because the data is looped over the filter block-by-block, the geometry cannot be considered fixed. This would allow the executive to spawn N-threads to execute on data and each core/thread can honour the flag.

Changes to the executive to support keys like GEOMETRY_NOT_MODIFIED will continue to work, even when a block is part of a multi-block structure.For example, the Exodus reader spends a lot of time appending pieces from separate files together. Encourages readers and filters to keep pieces separated, even when they belong to the same logical block (using vtkMultiPieceDataSet).Lots of parallelism for 'free.' We only implement the parallelism once in the pipeline and the simple filters automatically are parallelized.Since all of these blocks are in core anyway, one easy way to take advantage of this split of the data is to run the processing of each block in a different thread (since many algorithms are run independently on blocks anyway. We are also seeing more data being multiblock. Multiblock support within ParaView is getting better and better. There are multiple general approaches we can make. Thus, it is becoming vital that we natively support multicore processors. Right now, the only way to take advantage of the multiple cores is to launch a server locally, and that is a waste of resources on many levels. A very valid mode of operation that is becoming commonplace is the usage of a high end workshop with a fair number of cores (4-8) to do visualization of moderate size data sets (moderate in comparison to some of the large simulations done at Sandia). An even bigger problem is that this method only really works when running in client/server mode. This works OK, but it is not the most efficient use of these SMP parallel processes. So far, we have made use of multi core processors by running multiple process on each node to take advantage of the multiple cores as if they had separate memory. ParaView already supports efficient parallel processing with MPI, which is the 'hard' version of distributed memory parallel processing. This is where Kitware and VTK.js came in to help Quanscient build the UI.įeatures like translucent rendering and GPU selector already available in VTK.js combined with Kitware’s strong expertise in web visualization made it a breeze to come in with a powerful yet simple UI.In some sense, the parallelization of ParaView is done. Setting up a simulation can be done entirely through a scripting tool based on Python.Īdditionally, Quanscient.allsolve introduces a very user-friendly experience to interactively setup simulations. Only a web browser is needed to get started. Thanks to those algorithms as well as a distributed architecture, Quanscient.allsolve enables running simulations at blazing speed. Quanscient developed a set of high-performance algorithms for running parallel simulations on the cloud, including multiphysics and transient simulations.

#Paraview parallel software#

Quanscient is a Finland based company offering Quanscient.allsolve, a cutting-edge simulation software running in the cloud with a web-based UI.