Which parallel programming APIs do you use? [closed]

Answer 1

How about Open CL?

Answer 2

pycuda... nothing like 25000 active threads :) [warp scheduled with scoreboarding]. cuda 2 has stream support, so I'm not sure what streamit would bring. CUDA Matlab extensions look neat, as do PLUTO and the coming PetaBricks from MIT.

as far as others, python's threading is lacking; MPI, etc. are complicated, and I don't have a cluster, but I suppose they achieve what they are built for; I stopped c# programming before I got to thread apartments (probably a good thing).

Answer 3

Parallel FX Library (PFX) - a managed concurrency library being developed by a collaboration between Microsoft Research and the CLR team at Microsoft for inclusion with a future revision of the .NET Framework. It is composed of two parts: Parallel LINQ (PLINQ) and Task Parallel Library (TPL). It also consists of a set of Coordination Data Structures (CDS) - a set of data structures used to synchronize and co-ordinate the execution of concurrent tasks. The library was released as a CTP on November 29, 2007 and refreshed again in December 2007 and June 2008.

Not very much experience though...

Answer 4

MPI isn't as hard as most make it seem. Nowadays I think a multi-paradigm approach is best suited for parallel and distributed applications. Use MPI for your node to node communication and synchronization and either OpenMP or PThreads for your more granular parallelization. Think MPI for each machine, and OpenMP or PThreads for each core. This would seem to scale a little bit better than spawning a new MPI Proc for each core for the near future.

Perhaps for dual or quad core right now, spawning a proc for each core on a machine won't have that much overhead, but as we approach more and more cores per machine where the cache and on die memory aren't scaling as much, it would be more appropriate to use a shared memory model.

Answer 5

I use MPI and like it very much. It does force you to think about the memory hierarchy, but in my experience, thinking about such things is important for high performance anyway. In many cases, MPI can largely be hidden behind domain-specific parallel objects (e.g. PETSc for solving linear and nonlinear equations).

Answer 6

I used OpenMP alot mainly due to its simplicity, portability and flexibility. It's supports mulitple languages even almighty C++/Cli :)