Multi Core Processing

This page captures the initial thoughts on multi core processing. Currently, ParaView (basically) only supports MPI-based parallel processing. This type of parallelism concerns the latest generation of multicore processors that are common now.

Motivation

In some sense, the parallelization of ParaView is done. ParaView already supports efficient parallel processing with MPI, which is the "hard" version of distributed memory parallel processing. 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.

This works OK, but it is not the most efficient use of these SMP parallel processes. An even bigger problem is that this method only really works when running in client/server mode. 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). 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.

Thus, it is becoming vital that we natively support multicore processors.

Approaches

Multiblock parallelism

Multiblock support within ParaView is getting better and better. We are also seeing more data being multiblock. 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.

Pros:

• Lots of parallelism for "free." We only implement the parallelism once in the pipeline and the simple filters automatically are parallelized.

Cons:

• Only works for multiblocks and at whatever granularity the blocks in the data are stored. That does not exactly guarantee efficiency.
• 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. Efficiency may dictate writing special filters to handle the multiblock anyway.
• I do not expect very many filters to actually be thread safe. At the least, the initialization phase may be accessing and writing to some field variables. In the end, we may need to do a lot of editing on filters anyway.

Threading Methods

Threading Building Blocks

http://threadingbuildingblocks.org/ http://www.amazon.com/Intel-Threading-Building-Blocks-Parallelism/dp/0596514808/ref=pd_bbs_sr_1?ie=UTF8&s=books&qid=1204749345&sr=8-1