I have started working in a project using the PCL library under Windows 7. My question is if PCL provides any structures or algorithms for concurrent work. For example, creating a new point cloud from a data set concurrently; something like pcl::io::loadPCDFileKCores.
I have searched around in the API documentation and Google but find nothing.
Thanks a lot!.
PCL 1.7 does offer some facilities for exploting multiple processing cores on a system using GPU or CPU.
PCL uses multiple CPU cores on a system through the OpenMP API. You can check for multiple core enabled classes by searching PCL documentation for the "OpenMP" keyword (naive, but effective way!). As at the time of writing the reported OpenMP enabled classes are:
pcl::RangeImage
pcl::tracking::ParticleFilterOMPTracker
pcl::FPFHEstimationOMP
pcl::NormalEstimationOMP
pcl::Narf
pcl::tracking::KLDAdaptiveParticleFilterOMPTracker
pcl::SHOTColorEstimationOMP
pcl::SHOTEstimationOMP
pcl::NormalEstimationOMP< PointInT, Eigen::MatrixXf >
If you search the PCL documentation for the GPU or CUDA keywords a similar, but much longer, list of GPU-enabled classes is reported.
Related
I was reading the source code of opencv and I don't understand the function
CV_INSTRUMENT_REGION() called by other function. The CV_INSTRUMENT_REGION() function usually appears at the start of the other function body. Can anyone help? Thanks.
You can check the corresponding pull request for more information: Implementations instrumentation framework for OpenCV performance test system.
Instrumentation framework for OpenCV performance test system. It was
developed to:
track implementations execution inside OpenCV functions with call tree
output to test logs
track implementations execution time weight in the
pipeline
Framework can be enabled with ENABLE_INSTRUMENTATION define
in CMake and disabled by default. In disabled state it doesn't consume
resources.
Update:
OpenCV 3.3 added the support of trace / profiling OpenCV applications for users. For more information see:
the corresponding pull request: Code tracing support #8766
the Wiki page: Profiling OpenCV Applications
a sample code application_trace.cpp
Can anyone provide guidance on how to setup tensorflow to work on many CPUs across a network? All of the examples I have found thus far use only one local box and multi-gpus at best. I have found that I can pass in a list of targets in the session_opts, but I'm not sure how to setup tensorflow on each box to listen for networked nodes/tasks. Any example would be greatly appreciated!
The open-source version (currently 0.6.0) of TensorFlow supports single-process execution only: in particular, the only valid target in the tensorflow::SessionOptions is the empty string, which means "current process."
The TensorFlow whitepaper describes the structure of the distributed implementation (see Figure 3) that we use inside Google. The basic idea is that the Session interface can be implemented using RPC to a master; and the master can partition the computation across a set of devices in multiple worker processes, which also communicate using RPC. Alas, the current version depends heavily on Google-internal technologies (like Borg), so a lot of work remains to make it ready for external consumption. We are currently working on this, and you can follow the progress on this GitHub issue.
EDIT on 2/26/2016: Today we released an initial version of the distributed runtime to GitHub. It supports multiple machines and multiple GPUs.
I wrote a simple application that checks if NVIDIA CUDA is available on the computer. It simply displays true if a CUDA-capable device is found.
I send the app to a second PC, and the application didn't run - a dialog box showed up that cudart.dll was not found. I want to check if CUDA is present and it requires CUDA to do that :)
I am using CUDA 5.0, VS2012, VC++11, Windows 7.
Can I compile the application in a way, that all CUDA libraries are inside the executable?
So the scenario is:
My app is compiled & sent to a computer
The computer can:
be running windows, linux (my app is compatible with the system)
have a gpu or not
have an nvidia gpu or not
have CUDA installed or not
My app should return true only if 2.3 and 2.4 are positive (GPU with CUDA)
As an opening comment, I think the order and number of steps in your edit is incorrect. It should be:
Programs starts and attempts to load the runtime API library
If the runtime library is present, attempt to use it to enumerate devices.
If step 1 fails, you do not have the necessary runtime support, and CUDA cannot be used. If 2 fails, there is not a compatible driver and GPU present in the system and CUDA cannot be used. If they both pass, you are good to go.
In step 1 you want to use something like dlopen on Linux and handle the return status. On Windows, you probably want to use the DLL delay loading mechanism (Sorry, not a Windows programmer, can't tell you more than that).
In both cases, if the library loads, then fetch the address of cudaGetDeviceCount via the appropriate host OS API and call it. That tells you whether there are compatible GPUs which can be enumerated. What you do after you find an apparently usable GPU is up to you. I would check for compute status and try establishing a context on it. That will ensure that a fully functional runtime/driver combination is present and everything works.
Linking to a different post on stackoverflow: detecting-nvidia-gpus-without-cuda
This shows the whole sequence to check if the cuda api is available and accessible.
I think that using only the software there is no reliable way to ensure that a GPU is Cuda-capable or not, especially if we consider that Cuda is a driver-based technology and for the OS Cuda doesn't exist if the driver says that Cuda doesn't exist.
I think that the best way to do this is the old fashion way, consider checking this simple web page and you will get a much more reliable answer.
create a plugin for your application that dynamically links to the relevant CUDA-libraries and performs the check.
then try loading the plugin and run it's check.
if the plugin fails to load, then you don't have the CUDA-libraries installed, so you can assume False
if the plugin succeeds to load, then you have CUDA-libs installed and can perform the check, whether the hardware supports CUDA as well.
As a late andditional answer:
I am struggling with the same problem (detecting cuda installation without using it) and my solution so far is
ensuring LoadLibraryA("nvcuda.dll") != nullptr (tells you pretty much only if there is an nvidia card installed, though)
checking for environment variable CUDA_PATH (or in my case, CUDA_PATH_V8_0), since that seems to be set by the cuda installation: const char * szCuda8Path = std::getenv("CUDA_PATH_V8_0"); (must be != nullptr)
Use cudaGetDeviceCount() to know if the computer is CUDA-capable.
According to this thread, you cannot statically link cudart.dll.
There are workarounds: embed the CUDA runtime as a resource in your executable, then extract it when your program runs, then dynamically link.
You can also use nvidia-smi to see if CUDA is installed on a machine.
every one. i am a newbie to cuda. i am wondering that can cuda be used combining with ActiveX technology，
the presented ocx or dll file can be used in webpage，
for example， using cuda can we simulate a fluid particle easily.
if combine cuda and activeX technology ,
we can see fluid particle in a webpage, am i right?
what's more, if there are problems when i simulate lots of particles?
Thank you very much.
I think that if ActiveX could access your GPU on such low level as running your arbitrary CUDA code, it would be a big security risk. If on the other hand, ActiveX could perform some of its computations on the GPU though some higher-level interface, that would be safer, but it is Microsoft who would have to implement it, not you.
A trusted ActiveX control can do anything. So, yes, you could theoretically spin up the CUDA runtime and go to town with the GPU. You would need to distribute the CUDA runtime with the ActiveX control, but everything else you need would already be installed assuming they're using an nVidia GPU. FWIW, distributing cudart.dll is permissable per the EULA on the CUDA Developer Toolkit.
Since, last I read, you cannot statically link against cudart.dll, you would need to distribute that dependency along with your ActiveX control by using a CAB file. Details on creating CAB files can be found here on MSDN. Then again that forum post is from 2008, so maybe newer versions of cudart.dll can be statically linked now... you might want to give it a try.
First and foremost, it runs on the client machine. What means that the client needs to have a CUDA enabled graphics card (nVidia only).
Can some suggest a test/development embedded platform to use with OpenCV.
I would like to develop an embedded video analytics solution, but I don't know where to start.
Some suggestion/ideas/hw starter kits?
Maybe some Pc-104 solutions with Intel Atom? Has someone made some test about performances on this platform or any other embedded platform?
Thanks
A Pentium/PC built OpenCV application will run on any Atom platform with the same OS unmodified. This is because Atoms natively run Pentium executables.
If you are looking for a more embedded solution, there are OpenCV ports for the BeagleBoard. SInce OpenCV is portable code, it can be compiled to most systems that provide a C/C++ compiler. I have successfully used OpenCV on ARM, MIPS and XScale processors.
As for mobile platforms, there are ports to the iPhone, Android and various Windows CE/Mobile/Embdeed versions.
If you're looking for a very small option, I strongly recommend the Gumstix Overo series. I use them for my Computer Vision research, and they work really well. There are a couple of options for processors, I'd recommend the Overo Tide module, which has 512 MB of RAM, and an onboard DSP for offloading some CV operations. Combine this with a Tobi expansion board and a few cables, and you've got a full embedded computer vision research platform for ~$350. They also sell a small camera, which I'm still getting around to trying out. What's nice about the Gumstix is you can just build OpenCV onboard, which saves you some of the headaches with BitBake type solutions.
I'd personally recommend TI OMAP platforms - Beagleboard xM and PandaBoard.
Those boards have embedded video input, run Linux, and have more than enough performance to run OpenCV. They are also extremely portable and have good community support.
Do you mean OpenCV the computer vision library originally developed by Intel? I would be inclined to start with Moblin, Intel's embedded Linux, at moblin.org and for hw use a netbook or any PC that Moblin supports. Hook up a supported webcam from the list at www.qbik.ch/usb/devices/search_res.php?pattern=webcam .
There is a Wikipedia entry that might help. Your project sounds like fun!
cheers -- Rick
You can use the Blackfin kit from Analog Devices. Analog Devices have created a library similar to opencv for the blackfin DSP processor.
you can use Symbian Simulator for this they Nokia have there Open CV for Symbian for hardware testing you have to drop the mail to them they will provide u the hardware through the telnet for given time of time
OpenCV does not need any "special" hardware to function. You can use it fully using images from normal files (e.g. JPG)
Have you looked at some of the tutorials/code? Do they require something specific that you do not have?
Vision Components seem to support the OpenCV in their Smart Cameras (see this article).
I guess I am late to answer.
I have recently used opencv3.4.6 with PC-104 boards (PCM3365) for an INDUSTRIAL Application.
Only thing to note is that when i start webcamera using cv::Videocapture, it takes a long time to open (around 30-40secs), otherwise everything is fine.
Good Luck