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155 lines
4.6 KiB
TeX
155 lines
4.6 KiB
TeX
\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 the 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 the current device index, which was set by {gpu::getDevice} or initialized by default.
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\cvdefCpp{int getDevice();}
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\cvclass{gpu::GpuFeature}\label{cpp.gpu.GpuFeature}
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GPU compute features.
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\begin{lstlisting}
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enum GpuFeature
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{
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FEATURE_SET_COMPUTE_10, FEATURE_SET_COMPUTE_11,
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FEATURE_SET_COMPUTE_12, FEATURE_SET_COMPUTE_13,
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FEATURE_SET_COMPUTE_20, FEATURE_SET_COMPUTE_21,
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ATOMICS, NATIVE_DOUBLE
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};
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\end{lstlisting}
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\cvclass{gpu::DeviceInfo}
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This class provides functionality for querying the specified GPU properties.
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\begin{lstlisting}
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class CV_EXPORTS DeviceInfo
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{
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public:
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DeviceInfo();
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DeviceInfo(int device_id);
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string name() const;
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int majorVersion() const;
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int minorVersion() const;
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int multiProcessorCount() const;
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size_t freeMemory() const;
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size_t totalMemory() const;
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bool supports(GpuFeature feature) const;
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bool isCompatible() const;
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};
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\end{lstlisting}
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\cvCppFunc{gpu::DeviceInfo::DeviceInfo}
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Constructs DeviceInfo object for the specified device. If \texttt{device\_id} parameter is missed it constructs object for the current device.
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\cvdefCpp{DeviceInfo::DeviceInfo();\newline
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DeviceInfo::DeviceInfo(int device\_id);}
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\begin{description}
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\cvarg{device\_id}{Index of the GPU device in system starting with 0.}
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\end{description}
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\cvCppFunc{gpu::DeviceInfo::name}
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Returns the device name.
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\cvdefCpp{string DeviceInfo::name();}
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\cvCppFunc{gpu::DeviceInfo::majorVersion}
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Returns the major compute capability version.
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\cvdefCpp{int DeviceInfo::majorVersion();}
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\cvCppFunc{gpu::DeviceInfo::minorVersion}
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Returns the minor compute capability version.
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\cvdefCpp{int DeviceInfo::minorVersion();}
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\cvCppFunc{gpu::DeviceInfo::multiProcessorCount}
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Returns the number of streaming multiprocessors.
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\cvdefCpp{int DeviceInfo::multiProcessorCount();}
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\cvCppFunc{gpu::DeviceInfo::freeMemory}
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Returns the amount of free memory in bytes.
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\cvdefCpp{size\_t DeviceInfo::freeMemory();}
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\cvCppFunc{gpu::DeviceInfo::totalMemory}
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Returns the amount of total memory in bytes.
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\cvdefCpp{size\_t DeviceInfo::totalMemory();}
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\cvCppFunc{gpu::DeviceInfo::supports}
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Returns true if the device has the given GPU feature, otherwise false.
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\cvdefCpp{bool DeviceInfo::supports(GpuFeature feature);}
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\begin{description}
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\cvarg{feature}{Feature to be checked. See \hyperref[cpp.gpu.GpuFeature]{cv::gpu::GpuFeature}.}
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\end{description}
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\cvCppFunc{gpu::DeviceInfo::isCompatible}
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Returns true if the GPU module can be run on the specified device, otherwise false.
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\cvdefCpp{bool DeviceInfo::isCompatible();}
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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. See \hyperref[cpp.gpu.GpuFeature]{cv::gpu::GpuFeature}.}
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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(s):
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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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% 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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