shared-memory

I want to add network control of a handful of parameters used by a service (daemon) running on a Linux embedded system. There's no need for procedure calls, each parameter can be polled in a very natural way. Shared memory seems a nice way to keep networking code out of the daemon, and limit shared access to a carefully controlled set of variables. Since I don't want partial writes to cause visibility of values never written, I was thinking of using std::atomic<bool> and std::atomic<int> . However, I'm worried that std::atomic<T> might be implemented in a way that only works with C++11 threads

Lets say there is a computer with 4 CPUs each having 2 cores, so totally 8 cores. With my limited understanding I think that all processors share same memory in this case. Now, is it better to directly use openMP or to use MPI to make it general so that the code could work on both distributed and shared settings. Also, if I use MPI for a shared setting would performance decrease compared with openMP? Hristo Iliev With most distributed memory platforms nowadays consisting of SMP or NUMA nodes it just makes no sense to not use OpenMP. OpenMP and MPI can perfectly work together; OpenMP feeds the

Correct me if I'm wrong, but I'm surprised this hasn't been asked before on here ... It's a pretty simple difference. In a shared memory model, multiple workers all operate on the same data. This opens up a lot of the concurrency issues that are common in parallel programming. Message passing systems make workers communicate through a messaging system. Messages keep everyone seperated, so that workers cannot modify each other's data. By analogy, lets say we are working with a team on a project together. In one model, we are all crowded around a table, with all of our papers and data layed out.

I spawn a seperate process to handle my cloud services. I spawnb it like this: CldProc = Process(target=CloudRun) CldProc.start() and am wondering if I can have a shared dictionary between that CloudProc and my current main process? EDIT: Alternatively I am thinking to use pickle to dump my data into a file from the process and load it back, this requires me to use join() to wait for the process to complete and exit. 2nd EDIT So, I now have my dict declared like mac_dict={} and then I fill it in my subprocess and want to access it in my main process. Now I just tried this: >>> dict = dict() >>

I am new to Boost. I have the following structure and want to store it in shared memory using Boost. struct InData{ int x,y,h,w; char* lbl; }; In-turn this structure will be stored in Vector. Most of example talk about int or string datatype for Vectors. I would like if anbody can provide an example how to store user defined data type into boost shared memory. You can easily store UDT in there, Boost's interprocess allocator will do the magic for you. However, storing raw-pointers is not gonna work. So, let's start off with a sample without raw pointers: struct InData { int x = 0, y = 0, h = 0

I have a problem with placing boost::lockfree::queue<<T, fixed_sized<false>, ..> in shared memory. I need it because I have to be able to insert more than 65535 messages into the queue, and fixed_sized queue is limited with 65535. The following code works properly (but capacity<...> option implies fixed_sized<true> ): typedef boost::interprocess::allocator< MessageT, boost::interprocess::managed_shared_memory::segment_manager> ShmemAllocator; typedef boost::lockfree::queue< MessageT, boost::lockfree::capacity<65535>, boost::lockfree::allocator<ShmemAllocator> > Queue; m_segment = new boost:

Are there any guarantees on when a memory write in one thread becomes visible in other threads using pthread s? Comparing to Java, the Java language spec has a section that specifies the interaction of locks and memory that makes it possible to write portable multi-threaded Java code. Is there a corresponding pthreads spec? Sure, you can always go and make shared data volatile, but that is not what I'm after. If this is platform dependent, is there a de facto standard? Or should another threading library be used? POSIX specifies the memory model in 4.11 Memory Synchronization : Applications