One increasingly common approach in High Performance Computing (HPC) is hybrid MPI/OpenMP programs. I.e. you have N MPI processes, and each MPI process has M threads. This approach maps well to clusters consisting of shared memory multiprocessor nodes.

Changing to such a hierarchical parallelization scheme obviously requires some more or less invasive changes, OTOH if done properly it can increase the performance and scalability of the code in addition to reducing memory consumption for replicated data.

Depending on the MPI implementation, you may or may not be able to make MPI calls from all threads. This is specified by the required and provided arguments to the MPI_Init_Thread() function that you must call instead of MPI_Init(). Possible values are

{ MPI_THREAD_SINGLE}
    Only one thread will execute. 
{ MPI_THREAD_FUNNELED}
    The process may be multi-threaded, but only the main thread will make MPI calls (all MPI calls are ``funneled   to the main thread). 
{ MPI_THREAD_SERIALIZED}
    The process may be multi-threaded, and multiple threads may make MPI calls, but only one at a time: MPI calls are not made concurrently from two distinct threads (all MPI calls are ``serialized  ). 
{ MPI_THREAD_MULTIPLE}
    Multiple threads may call MPI, with no restrictions. 

In my experience, modern MPI implementations like Open MPI support the most flexible MPI_THREAD_MULTIPLE. If you use older MPI libraries, or some specialized architecture, you might be worse off.

Of course, you don t need to do your threading with OpenMP, that s just the most popular option in HPC. You could use e.g. the Boost threads library, the Intel TBB library, or straight pthreads or windows threads for that matter.

I haven t worked with MPI, but if it s like other IPC libraries I ve seen that hide whether other threads/processes/whatever are on the same or different machines, then it won t be able to guarantee shared memory. Yes, it could handle shared memory between two nodes on the same machine, if that machine provided shared memory itself. But trying to share memory between nodes on different machines would be very difficult at best, due to the complex coherency issues raised. I d expect it to simply be unimplemented.

In all practicality, if you need to share memory between nodes, your best bet is to do that outside MPI. i don t think you need to use boost.interprocess-style shared memory, since you aren t describing a situation where the different nodes are making fine-grained changes to the shared memory; it s either read-only or partitioned.

John s and deus s answers cover how to map in a file, which is definitely what you want to do for the 5 Gb (gigabit?) static data. The per-CPU data sounds like the same thing, and you just need to send a message to each node telling it what part of the file it should grab. The OS should take care of mapping virtual memory to physical memory to the files.

As for cleanup... I would assume it doesn t do any cleanup of shared memory, but mmaped files should be cleaned up since files are closed (which should release their memory mappings) when a process is cleaned up. I have no idea what caveats CreateFileMapping etc. have.

Actual "shared memory" (i.e. boost.interprocess) is not cleaned up when a process dies. If possible, I d recommend trying killing a process and seeing what is left behind.

With MPI-2 you have RMA (remote memory access) via functions such as MPI_Put and MPI_Get. Using these features, if your MPI installation supports them, would certainly help you reduce the total memory consumption of your program. The cost is added complexity in coding but that s part of the fun of parallel programming. Then again, it does keep you in the domain of MPI.

MPI-3 offers shared memory windows (see e.g. MPI_Win_allocate_shared()), which allows usage of on-node shared memory without any additional dependencies.

I don t know much about unix, and I don t know what MPI is. But in Windows, what you are describing is an exact match for a file mapping object.

If this data is imbedded in your .EXE or a .DLL that it loads, then it will automatically be shared between all processes. Teardown of your process, even as a result of a crash will not cause any leaks or unreleased locks of your data. however a 9Gb .dll sounds a bit iffy. So this probably doesn t work for you.

However, you could put your data into a file, then CreateFileMapping and MapViewOfFile on it. The mapping can be readonly, and you can map all or part of the file into memory. All processes will share pages that are mapped the same underlying CreateFileMapping object. it s good practice to close unmap views and close handles, but if you don t the OS will do it for you on teardown.

Note that unless you are running x64, you won t be able to map a 5Gb file into a single view (or even a 2Gb file, 1Gb might work). But given that you are talking about having this already working, I m guessing that you are already x64 only.

If you store your static data in a file, you can use mmap on unix to get random access to the data. Data will be paged in as and when you need access to a particular bit of the data. All that you will need to do is overlay any binary structures over the file data. This is the unix equivalent of CreateFileMapping and MapViewOfFile mentioned above.

Incidentally glibc uses mmap when one calls malloc to request more than a page of data.

I had some projects with MPI in SHUT.

As i know , there are many ways to distribute a problem using MPI, maybe you can find another solution that does not required share memory, my project was solving an 7,000,000 equation and 7,000,000 variable

if you can explain your problem,i would try to help you

I ran into this problem in the small when I used MPI a few years ago.

I am not certain that the SGE understands memory mapped files. If you are distributing against a beowulf cluster, I suspect you re going to have coherency issues. Could you discuss a little about your multiprocessor architecture?

My draft approach would be to set up an architecture where each part of the data is owned by a defined CPU. There would be two threads: one thread being an MPI two-way talker and one thread for computing the result. Note that MPI and threads don t always play well together.