On Thu, Jun 25, 2015 at 10:25 AM, Sturla Molden <sturla.molden@gmail.com> wrote:
On 25/06/15 16:31, Nick Coghlan wrote:
3. The potential for collisions between objects means it isn't an
embarrassingly parallel problem where the different computational threads can entirely ignore the existence of the other threads
Well, you can have a loop that updates all particles, e.g. by calling a coroutine associated with each particle, and then this loop is an embarrassingly parallel problem. You don't need to associate each particle with its own thread.
It is bad to teach students to use one thread per particle anyway. Suddenly they write a system that have thousands of threads.
Understood that this is merely an example re: threading, but BSP seems to be the higher-level algorithm for iterative graphs with topology: * https://en.wikipedia.org/wiki/Bulk_synchronous_parallel * http://googleresearch.blogspot.com/2009/06/large-scale-graph-computing-at-go... * https://giraph.apache.org/ * https://spark.apache.org/docs/latest/ * https://spark.apache.org/docs/latest/graphx-programming-guide.html#pregel-ap... (BSP) * https://spark.apache.org/news/spark-wins-daytona-gray-sort-100tb-benchmark.h... * https://spark.apache.org/docs/latest/api/python/ (no graphx BSP yet, unfortunately) * https://github.com/xslogic/phoebus (Erlang, HDFS, Thrift) * https://github.com/mnielsen/Pregel/blob/master/pregel.py (Python) Intra-machine optimization could also be useful.