tbb

Order-preserving selection from an index table is trivial in serial code, but in multi-threading is less straightforward, in particular if one wants to retain efficiency (the whole point of multi-threading) by avoiding linked lists. Consider the serial code template<typename T> std::vector<T> select_in_order( std::vector<std::size_t> const&keys, // permutation of 0 ... key.size()-1 std::vector<T> const&data) // anything copyable { // select data[keys[i]] allowing keys.size() >= data.size() std::vector<T> result; for(auto key:keys) if(key<data.size()) result.push_back(data[key]); return result;

In my previous question, I implemented a TBB Pipeline using C++ (Linux) with Input, Transform and Output filters: incorrect output with TBB pipeline The Input filter was reading data (C structure) from text files and was passing to Transform filter. The Transform filter was updating data and passing it to Output filter. The Output filter was storing it back on the disc. A simple read and write application. Now, I am looking to create a MAIN application. The MAIN application will create a TBB Pipeline with three filters in the start: InputFilter : Receive data/C structure from the main

Is it possible to pass a class member function (with parameters) to parallel_for? Something along the lines: void classT::A(const tbb::blocked_range<std::size_t>& r,b) {} void classT::B(e,f,g) { tbb::parallel_for( blocked_range<size_t>(0,n), <need to call A with parameter b, along with r> ) } Look at this example . They create a class and suply the class with a all the parameters needed. In that class is also a operator() which does an oppertion on the data. The parallel_for is then called with a instance of that class. parallel_for(blocked_range<int>(0, nElements, 100), ArraySummer( p_A, p_B,

Basically I am developing an opencv application. I have built OpenCV with_tbb option in cmake . I would like to use intel tbb to run a parallel thread that at some intervals updates some global variables. Something like: vector<int> mySharedVar; void secondaryThreadFunction() { while(true) { Do some operations And update mySharedVar if necessarily usleep(1000); } } int main() { run in parallel secondaryThreadFunction; in the master Thread keep doing something based on mySharedVar while(true) { do something; } } How can I run secondaryThreadFunction() on another thread ? Intel TBB is not meant

Can you give me an example on tbb "parallel_for" without using lambda expression? Because I can't run lambda expression under Ubuntu system's C++ compiler, and I don't why. to be brief: turn this for loop into parallel_for please. void print(int n) { cout<<n<<endl; } for(int i=0; i<100; i++) { print(i); } by the way, if who can tell me how to run C++ lambda expression in linux system, that would be better for me. Thanks. parallel_for will take any functor, which can be a lambda, a functor class or a plain old function; the following should work just fine too: #include "tbb/tbb.h" using

I'm only asking this to try to understand what I've spent 24 hours trying to fix. My system: Ubuntu 12.04.2, Matlab R2011a, both of them 64-bit, Intel Xeon processor based on Nehalem. The problem is simply, Matlab allows OpenMP based programs to utilize all CPU cores with hyper-threading enabled but does not allow the same for TBB. When running TBB, I can launch only 4 threads, even when I change the maxNumCompThreads to 8. While with OpenMP I can use all the threads I want. Without Hyper-threading, both TBB and OpenMP utilize all 4 cores of course. I understand Hyper-threading and that its

I want to execute a for-loop in parallel (using TBB) over a set of blocks, where each block will be processed using a user-supplied function. Normally, I would do this using tbb::parallel_for() . For various reasons, I want to be able to limit the number of threads processing the blocks to a prescribed number, call it j . Normally, I would do this using tbb::task_scheduler_init(j) . However, I would like the user to have the option to use TBB and, specifically, let the user-supplied function use however many cores remain. So I think tbb::task_scheduler_init() is out. The only solution I can