mirror of
https://github.com/opencv/opencv.git
synced 2024-12-01 14:59:54 +08:00
155 lines
4.4 KiB
TeX
155 lines
4.4 KiB
TeX
\section{Initalization and Information}
|
|
|
|
|
|
\cvCppFunc{gpu::getCudaEnabledDeviceCount}
|
|
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.
|
|
|
|
\cvdefCpp{int getCudaEnabledDeviceCount();}
|
|
|
|
|
|
\cvCppFunc{gpu::setDevice}
|
|
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.
|
|
|
|
\cvdefCpp{void setDevice(int device);}
|
|
\begin{description}
|
|
\cvarg{device}{index of GPU device in system starting with 0.}
|
|
\end{description}
|
|
|
|
|
|
\cvCppFunc{gpu::getDevice}
|
|
Returns the current device index, which was set by {gpu::getDevice} or initialized by default.
|
|
|
|
\cvdefCpp{int getDevice();}
|
|
|
|
|
|
\cvclass{gpu::GpuFeature}\label{cpp.gpu.GpuFeature}
|
|
GPU compute features.
|
|
|
|
\begin{lstlisting}
|
|
enum GpuFeature
|
|
{
|
|
COMPUTE_10, COMPUTE_11,
|
|
COMPUTE_12, COMPUTE_13,
|
|
COMPUTE_20, COMPUTE_21,
|
|
ATOMICS, NATIVE_DOUBLE
|
|
};
|
|
\end{lstlisting}
|
|
|
|
|
|
\cvclass{gpu::DeviceInfo}
|
|
This class provides functionality for querying the specified GPU properties.
|
|
|
|
\begin{lstlisting}
|
|
class CV_EXPORTS DeviceInfo
|
|
{
|
|
public:
|
|
DeviceInfo();
|
|
DeviceInfo(int device_id);
|
|
|
|
string name() const;
|
|
|
|
int major() const;
|
|
int minor() const;
|
|
|
|
int multiProcessorCount() const;
|
|
|
|
size_t freeMemory() const;
|
|
size_t totalMemory() const;
|
|
|
|
bool has(GpuFeature feature) const;
|
|
bool isCompatible() const;
|
|
};
|
|
\end{lstlisting}
|
|
|
|
|
|
\cvCppFunc{gpu::DeviceInfo::DeviceInfo}
|
|
Constructs DeviceInfo object for the specified device. If \texttt{device\_id} parameter is missed it constructs object for the current device.
|
|
|
|
\cvdefCpp{DeviceInfo::DeviceInfo();\newline
|
|
DeviceInfo::DeviceInfo(int device\_id);}
|
|
\begin{description}
|
|
\cvarg{device\_id}{Index of the GPU device in system starting with 0.}
|
|
\end{description}
|
|
|
|
|
|
\cvCppFunc{gpu::DeviceInfo::name}
|
|
Returns the device name.
|
|
|
|
\cvdefCpp{string DeviceInfo::name();}
|
|
|
|
|
|
\cvCppFunc{gpu::DeviceInfo::major}
|
|
Returns the major compute capability version.
|
|
|
|
\cvdefCpp{int DeviceInfo::major();}
|
|
|
|
|
|
\cvCppFunc{gpu::DeviceInfo::minor}
|
|
Returns the minor compute capability version.
|
|
|
|
\cvdefCpp{int DeviceInfo::minor();}
|
|
|
|
|
|
\cvCppFunc{gpu::DeviceInfo::multiProcessorCount}
|
|
Returns the number of streaming multiprocessors.
|
|
|
|
\cvdefCpp{int DeviceInfo::multiProcessorCount();}
|
|
|
|
|
|
\cvCppFunc{gpu::DeviceInfo::freeMemory}
|
|
Returns the amount of free memory in bytes.
|
|
|
|
\cvdefCpp{size\_t DeviceInfo::freeMemory();}
|
|
|
|
|
|
\cvCppFunc{gpu::DeviceInfo::totalMemory}
|
|
Returns the amount of total memory in bytes.
|
|
|
|
\cvdefCpp{size\_t DeviceInfo::totalMemory();}
|
|
|
|
|
|
\cvCppFunc{gpu::DeviceInfo::has}
|
|
Returns true if the device has the given GPU feature, otherwise false.
|
|
|
|
\cvdefCpp{bool DeviceInfo::has(GpuFeature feature);}
|
|
\begin{description}
|
|
\cvarg{feature}{Feature to be checked. See \hyperref[cpp.gpu.GpuFeature]{gpu::GpuFeature}.}
|
|
\end{description}
|
|
|
|
|
|
\cvCppFunc{gpu::DeviceInfo::isCompatible}
|
|
Returns true if the GPU module can be run on the specified device, otherwise false.
|
|
|
|
\cvdefCpp{bool DeviceInfo::isCompatible();}
|
|
|
|
|
|
\cvclass{gpu::TargetArchs}
|
|
This class provides functionality (as set of static methods) for checking which NVIDIA card architectures the GPU module was built for.
|
|
|
|
\bigskip
|
|
|
|
The following method checks whether the module was built with the support of the given feature:
|
|
\cvdefCpp{static bool builtWith(GpuFeature feature);}
|
|
\begin{description}
|
|
\cvarg{feature}{Feature to be checked. See \hyperref[cpp.gpu.GpuFeature]{gpu::GpuFeature}.}
|
|
\end{description}
|
|
|
|
There are a set of methods for checking whether the module contains intermediate (PTX) or binary GPU code for the given architecture(s):
|
|
\cvdefCpp{
|
|
static bool has(int major, int minor);\newline
|
|
static bool hasPtx(int major, int minor);\newline
|
|
static bool hasBin(int major, int minor);\newline
|
|
static bool hasEqualOrLessPtx(int major, int minor);\newline
|
|
static bool hasEqualOrGreater(int major, int minor);\newline
|
|
static bool hasEqualOrGreaterPtx(int major, int minor);\newline
|
|
static bool hasEqualOrGreaterBin(int major, int minor);}
|
|
\begin{description}
|
|
\cvarg{major}{Major compute capability version.}
|
|
\cvarg{minor}{Minor compute capability version.}
|
|
\end{description}
|
|
|
|
% By default GPU module is no compiled for devices with compute capability equal to 1.0. So if you run
|
|
|
|
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".
|
|
|