Arrakis's profile - activity

I'm afraid that doesn't make sense. Performance hasn't improved, but I haven't made any changes.

I have a separate SVM data structure copy for each thread. Instantiation code:

static std::vector<cv::SVM> shared_svm;
shared_svm.resize(num_threads);
shared_svm[i].load(classifier_path.c_str());

Classification code:

static void processThread(const std::size_t i)
{
    const cv::SVM &svm = shared_svm[i];
    ...
    const CvMat predictor_old = ...
    CvMat *responses_old_ptr = cvCreateMat(rows, 1, CV_32FC1);
    svm.predict(&predictor_old, responses_old_ptr);

I should add that this situation hasn't changed in version 2.4.3, despite the promise of better multicore performance. Compiling with TBB on actually gave worse performance than with if off.

Hello all,

I am using a SVM in (what should be) a 30hz application using Ubuntu 10.4 and OpenCV 2.4. I need to do approximately 500 classifications per frame. Even using a linear SVM predict for all 500 samples is very slow for me. I have profiled that cv::SVM takes 75% of all computation time in my program, and it's only running at 17FPS. However only about 1.5 cores of my 4 core CPU are being utilised (running top gives 160%). Programs like GNU Parallel result in ~370% of my CPU being utilised.

My problem is that multithreading the SVM prediction does not give a performance boost. I have tried both the SVM predict API that uses cv::parallel_for, and the API which does not. Using 1 thread gives around 14FPS, using 2 threads gives around 17FPS, and using more still gives ~17FPS.

My Question: Why is the SVM prediction slow, and yet still only using less than half of my CPU cores? Why does manually multithreading and doing half the predictions between 2 threads give only a small speedup? Is the OpenCV SVM just not very fast and should I use another implementation?

Many thanks