Parallel with Microsoft R open (MKL), but no improvement

Question

I'm running an MCMC algorithm and Microsoft R open on Windows 7 has improved my speed a lot. But right now I need to run tons of simulations using my algorithm, so I used the R snow package to parallel my code. However, it doesn't work.

To be specific, the Microsfot R open on my PC is using 4 cores for calculation, while there are 8 cores in total. So I'm thinking I will parallel 2 process on my PC since each will need 4 cores for MKL library. But the parallel isn't real at all. I set up all my 8 cores when paralleling. My test program will need 5 minutes to run. But if I'm paralleling my program with a copy of that, I hope the 2 process will take 5 minutes as well. But actually it took 10 minutes, just like running the 2 process sequentially.

The same thing happened if I tried to open two R sessions and run the programs in the two R sessions. Usually it will only need 5 mins, but now each of them will take 10 mins.

So where am I messing up? Is that the problems about two layers of parallel? One is at my level, the other one is at the intel MKL level?

Answer 1

There are way too many factors at play here to figure it out without knowing certain details about your code. For example, what is the affinity mask in effect for each process? What is the Tread Ideal Processor for threads in concurrent processes? It is possible, that your processes are trying to compete for the same cores. You can find more details by looking at the SetThreadIdealProcessor and SetProcessAffinityMask APIs. It is also possible that your code is using a shared resource protected by a critical section or other synchronization object. I would start by downloading Process Explorer from Sysinternals and looking at the thread list for each process. This would tell you how many physical threads are running and how many context switches are there for each thread. This will give you something to start with.