Open Source Computer Vision Library
https://opencv.org/
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111 lines
4.0 KiB
111 lines
4.0 KiB
\section{Initalization and Information} |
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\cvCppFunc{gpu::getCudaEnabledDeviceCount} |
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Returns number of CUDA-enabled devices installed. It is to be used before any other GPU functions calls. If OpenCV is compiled without GPU support this function returns 0. |
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\cvdefCpp{int getCudaEnabledDeviceCount();} |
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\cvCppFunc{gpu::setDevice} |
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Sets device and initializes it for current thread. Call of this function can be omitted, but in this case a default device will be initialized on fist GPU usage. |
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\cvdefCpp{void setDevice(int device);} |
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\begin{description} |
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\cvarg{device}{index of GPU device in system starting with 0.} |
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\end{description} |
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\cvCppFunc{gpu::getDevice} |
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Returns current device index, which was set by \cvCppCross{gpu::getDevice} of initialized by default. |
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\cvdefCpp{int getDevice();} |
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\cvCppFunc{gpu::getComputeCapability} |
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Returns compute capability version for given device. |
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\cvdefCpp{void getComputeCapability(int device, int\& major, int\& minor);} |
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\begin{description} |
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\cvarg{device}{GPU index. Can be obtained via \cvCppCross{gpu::getDevice}.} |
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\cvarg{major}{Major CC version.} |
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\cvarg{minor}{Minor CC version.} |
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\end{description} |
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\cvCppFunc{gpu::getNumberOfSMs} |
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Returns number of Streaming Multiprocessors for given device. |
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\cvdefCpp{int getNumberOfSMs(int device);} |
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\begin{description} |
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\cvarg{device}{GPU index. Can be obtained via \cvCppCross{gpu::getDevice}.} |
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\end{description} |
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\cvCppFunc{gpu::getGpuMemInfo} |
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Returns free and total memory for the current device. |
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\cvdefCpp{void getGpuMemInfo(size\_t\& free, size\_t\& total);} |
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\begin{description} |
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\cvarg{free}{Reference to free GPU memory counter.} |
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\cvarg{total}{Reference to total GPU memory counter.} |
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\end{description} |
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\cvCppFunc{gpu::hasNativeDoubleSupport} |
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Returns true, if the specified GPU has native double support, otherwise false. |
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\cvdefCpp{bool hasNativeDoubleSupport(int device);} |
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\begin{description} |
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\cvarg{device}{GPU index. Can be obtained via \cvCppCross{gpu::getDevice}.} |
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\end{description} |
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\cvCppFunc{gpu::hasAtomicsSupport} |
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Returns true, if the specified GPU has atomics support, otherwise false. |
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\cvdefCpp{bool hasAtomicsSupport(int device);} |
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\begin{description} |
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\cvarg{device}{GPU index. Can be obtained via \cvCppCross{gpu::getDevice}.} |
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\end{description} |
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\cvclass{gpu::TargetArchs} |
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This class provides functionality (as set of static methods) for checking which NVIDIA card architectures the GPU module was built for. |
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\bigskip |
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The following method checks whether the module was built with the support of the given feature: |
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\cvdefCpp{static bool builtWith(GpuFeature feature);} |
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\begin{description} |
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\cvarg{feature}{Feature to be checked. Available alternatives: |
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\begin{itemize} |
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\item NATIVE\_DOUBLE Native double operations support |
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\item ATOMICS Atomic operations support |
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\end{itemize}} |
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\end{description} |
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There are a set of methods for checking whether the module contains intermediate (PTX) or binary GPU code for the given architecture: |
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\cvdefCpp{ |
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static bool has(int major, int minor);\newline |
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static bool hasPtx(int major, int minor);\newline |
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static bool hasBin(int major, int minor);\newline |
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static bool hasEqualOrLessPtx(int major, int minor);\newline |
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static bool hasEqualOrGreater(int major, int minor);\newline |
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static bool hasEqualOrGreaterPtx(int major, int minor);\newline |
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static bool hasEqualOrGreaterBin(int major, int minor);} |
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\begin{description} |
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\cvarg{major}{Major compute capability version.} |
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\cvarg{minor}{Minor compute capability version.} |
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\end{description} |
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\cvCppFunc{gpu::isCompatibleWith} |
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Returns true, if the GPU module is built with PTX or CUBIN compatible with the given GPU device, otherwise false. |
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\cvdefCpp{bool isCompatibleWith(int device);} |
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\begin{description} |
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\cvarg{device}{GPU index. Can be obtained via \cvCppCross{gpu::getDevice}.} |
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\end{description} |
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% By default GPU module is no compiled for devices with compute capability equal to 1.0. So if you run |
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According to the CUDA C Programming Guide Version 3.2: "PTX code produced for some specific compute capability can always be compiled to binary code of greater or equal compute capability". |
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