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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::gpu;
namespace
{
// Compares value to set using the given comparator. Returns true if
// there is at least one element x in the set satisfying to: x cmp value
// predicate.
template <typename Comparer>
bool compareToSet(const std::string& set_as_str, int value, Comparer cmp)
{
if (set_as_str.find_first_not_of(" ") == string::npos)
return false;
std::stringstream stream(set_as_str);
int cur_value;
while (!stream.eof())
{
stream >> cur_value;
if (cmp(cur_value, value))
return true;
}
return false;
}
}
bool cv::gpu::TargetArchs::builtWith(cv::gpu::FeatureSet feature_set)
{
#if defined (HAVE_CUDA)
return ::compareToSet(CUDA_ARCH_FEATURES, feature_set, std::greater_equal<int>());
#else
(void)feature_set;
return false;
#endif
}
bool cv::gpu::TargetArchs::has(int major, int minor)
{
return hasPtx(major, minor) || hasBin(major, minor);
}
bool cv::gpu::TargetArchs::hasPtx(int major, int minor)
{
#if defined (HAVE_CUDA)
return ::compareToSet(CUDA_ARCH_PTX, major * 10 + minor, std::equal_to<int>());
#else
(void)major;
(void)minor;
return false;
#endif
}
bool cv::gpu::TargetArchs::hasBin(int major, int minor)
{
#if defined (HAVE_CUDA)
return ::compareToSet(CUDA_ARCH_BIN, major * 10 + minor, std::equal_to<int>());
#else
(void)major;
(void)minor;
return false;
#endif
}
bool cv::gpu::TargetArchs::hasEqualOrLessPtx(int major, int minor)
{
#if defined (HAVE_CUDA)
return ::compareToSet(CUDA_ARCH_PTX, major * 10 + minor,
std::less_equal<int>());
#else
(void)major;
(void)minor;
return false;
#endif
}
bool cv::gpu::TargetArchs::hasEqualOrGreater(int major, int minor)
{
return hasEqualOrGreaterPtx(major, minor) ||
hasEqualOrGreaterBin(major, minor);
}
bool cv::gpu::TargetArchs::hasEqualOrGreaterPtx(int major, int minor)
{
#if defined (HAVE_CUDA)
return ::compareToSet(CUDA_ARCH_PTX, major * 10 + minor,
std::greater_equal<int>());
#else
(void)major;
(void)minor;
return false;
#endif
}
bool cv::gpu::TargetArchs::hasEqualOrGreaterBin(int major, int minor)
{
#if defined (HAVE_CUDA)
return ::compareToSet(CUDA_ARCH_BIN, major * 10 + minor,
std::greater_equal<int>());
#else
(void)major;
(void)minor;
return false;
#endif
}
#if !defined (HAVE_CUDA)
int cv::gpu::getCudaEnabledDeviceCount() { return 0; }
void cv::gpu::setDevice(int) { throw_nogpu(); }
int cv::gpu::getDevice() { throw_nogpu(); return 0; }
void cv::gpu::resetDevice() { throw_nogpu(); }
size_t cv::gpu::DeviceInfo::freeMemory() const { throw_nogpu(); return 0; }
size_t cv::gpu::DeviceInfo::totalMemory() const { throw_nogpu(); return 0; }
bool cv::gpu::DeviceInfo::supports(cv::gpu::FeatureSet) const { throw_nogpu(); return false; }
bool cv::gpu::DeviceInfo::isCompatible() const { throw_nogpu(); return false; }
void cv::gpu::DeviceInfo::query() { throw_nogpu(); }
void cv::gpu::DeviceInfo::queryMemory(size_t&, size_t&) const { throw_nogpu(); }
void cv::gpu::printCudaDeviceInfo(int) { throw_nogpu(); }
void cv::gpu::printShortCudaDeviceInfo(int) { throw_nogpu(); }
#else /* !defined (HAVE_CUDA) */
int cv::gpu::getCudaEnabledDeviceCount()
{
int count;
cudaError_t error = cudaGetDeviceCount( &count );
if (error == cudaErrorInsufficientDriver)
return -1;
if (error == cudaErrorNoDevice)
return 0;
cudaSafeCall(error);
return count;
}
void cv::gpu::setDevice(int device)
{
cudaSafeCall( cudaSetDevice( device ) );
}
int cv::gpu::getDevice()
{
int device;
cudaSafeCall( cudaGetDevice( &device ) );
return device;
}
void cv::gpu::resetDevice()
{
cudaSafeCall( cudaDeviceReset() );
}
size_t cv::gpu::DeviceInfo::freeMemory() const
{
size_t free_memory, total_memory;
queryMemory(free_memory, total_memory);
return free_memory;
}
size_t cv::gpu::DeviceInfo::totalMemory() const
{
size_t free_memory, total_memory;
queryMemory(free_memory, total_memory);
return total_memory;
}
bool cv::gpu::DeviceInfo::supports(cv::gpu::FeatureSet feature_set) const
{
int version = majorVersion() * 10 + minorVersion();
return version >= feature_set;
}
bool cv::gpu::DeviceInfo::isCompatible() const
{
// Check PTX compatibility
if (TargetArchs::hasEqualOrLessPtx(majorVersion(), minorVersion()))
return true;
// Check BIN compatibility
for (int i = minorVersion(); i >= 0; --i)
if (TargetArchs::hasBin(majorVersion(), i))
return true;
return false;
}
void cv::gpu::DeviceInfo::query()
{
cudaDeviceProp prop;
cudaSafeCall(cudaGetDeviceProperties(&prop, device_id_));
name_ = prop.name;
multi_processor_count_ = prop.multiProcessorCount;
majorVersion_ = prop.major;
minorVersion_ = prop.minor;
}
void cv::gpu::DeviceInfo::queryMemory(size_t& free_memory, size_t& total_memory) const
{
int prev_device_id = getDevice();
if (prev_device_id != device_id_)
setDevice(device_id_);
cudaSafeCall(cudaMemGetInfo(&free_memory, &total_memory));
if (prev_device_id != device_id_)
setDevice(prev_device_id);
}
namespace
{
template <class T> void getCudaAttribute(T *attribute, CUdevice_attribute device_attribute, int device)
{
*attribute = T();
CUresult error = CUDA_SUCCESS;// = cuDeviceGetAttribute( attribute, device_attribute, device ); why link erros under ubuntu??
if( CUDA_SUCCESS == error )
return;
printf("Driver API error = %04d\n", error);
cv::gpu::error("driver API error", __FILE__, __LINE__);
}
int convertSMVer2Cores(int major, int minor)
{
// Defines for GPU Architecture types (using the SM version to determine the # of cores per SM
typedef struct {
int SM; // 0xMm (hexidecimal notation), M = SM Major version, and m = SM minor version
int Cores;
} SMtoCores;
SMtoCores gpuArchCoresPerSM[] = { { 0x10, 8 }, { 0x11, 8 }, { 0x12, 8 }, { 0x13, 8 }, { 0x20, 32 }, { 0x21, 48 }, { -1, -1 } };
int index = 0;
while (gpuArchCoresPerSM[index].SM != -1)
{
if (gpuArchCoresPerSM[index].SM == ((major << 4) + minor) )
return gpuArchCoresPerSM[index].Cores;
index++;
}
printf("MapSMtoCores undefined SMversion %d.%d!\n", major, minor);
return -1;
}
}
void cv::gpu::printCudaDeviceInfo(int device)
{
int count = getCudaEnabledDeviceCount();
bool valid = (device >= 0) && (device < count);
int beg = valid ? device : 0;
int end = valid ? device+1 : count;
printf("*** CUDA Device Query (Runtime API) version (CUDART static linking) *** \n\n");
printf("Device count: %d\n", count);
int driverVersion = 0, runtimeVersion = 0;
cudaSafeCall( cudaDriverGetVersion(&driverVersion) );
cudaSafeCall( cudaRuntimeGetVersion(&runtimeVersion) );
const char *computeMode[] = {
"Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)",
"Exclusive (only one host thread in one process is able to use ::cudaSetDevice() with this device)",
"Prohibited (no host thread can use ::cudaSetDevice() with this device)",
"Exclusive Process (many threads in one process is able to use ::cudaSetDevice() with this device)",
"Unknown",
NULL
};
for(int dev = beg; dev < end; ++dev)
{
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, dev) );
printf("\nDevice %d: \"%s\"\n", dev, prop.name);
printf(" CUDA Driver Version / Runtime Version %d.%d / %d.%d\n", driverVersion/1000, driverVersion%100, runtimeVersion/1000, runtimeVersion%100);
printf(" CUDA Capability Major/Minor version number: %d.%d\n", prop.major, prop.minor);
printf(" Total amount of global memory: %.0f MBytes (%llu bytes)\n", (float)prop.totalGlobalMem/1048576.0f, (unsigned long long) prop.totalGlobalMem);
printf(" (%2d) Multiprocessors x (%2d) CUDA Cores/MP: %d CUDA Cores\n",
prop.multiProcessorCount, convertSMVer2Cores(prop.major, prop.minor),
convertSMVer2Cores(prop.major, prop.minor) * prop.multiProcessorCount);
printf(" GPU Clock Speed: %.2f GHz\n", prop.clockRate * 1e-6f);
// This is not available in the CUDA Runtime API, so we make the necessary calls the driver API to support this for output
int memoryClock, memBusWidth, L2CacheSize;
getCudaAttribute<int>( &memoryClock, CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE, dev );
getCudaAttribute<int>( &memBusWidth, CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH, dev );
getCudaAttribute<int>( &L2CacheSize, CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE, dev );
printf(" Memory Clock rate: %.2f Mhz\n", memoryClock * 1e-3f);
printf(" Memory Bus Width: %d-bit\n", memBusWidth);
if (L2CacheSize)
printf(" L2 Cache Size: %d bytes\n", L2CacheSize);
printf(" Max Texture Dimension Size (x,y,z) 1D=(%d), 2D=(%d,%d), 3D=(%d,%d,%d)\n",
prop.maxTexture1D, prop.maxTexture2D[0], prop.maxTexture2D[1],
prop.maxTexture3D[0], prop.maxTexture3D[1], prop.maxTexture3D[2]);
printf(" Max Layered Texture Size (dim) x layers 1D=(%d) x %d, 2D=(%d,%d) x %d\n",
prop.maxTexture1DLayered[0], prop.maxTexture1DLayered[1],
prop.maxTexture2DLayered[0], prop.maxTexture2DLayered[1], prop.maxTexture2DLayered[2]);
printf(" Total amount of constant memory: %u bytes\n", (int)prop.totalConstMem);
printf(" Total amount of shared memory per block: %u bytes\n", (int)prop.sharedMemPerBlock);
printf(" Total number of registers available per block: %d\n", prop.regsPerBlock);
printf(" Warp size: %d\n", prop.warpSize);
printf(" Maximum number of threads per block: %d\n", prop.maxThreadsPerBlock);
printf(" Maximum sizes of each dimension of a block: %d x %d x %d\n", prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
printf(" Maximum sizes of each dimension of a grid: %d x %d x %d\n", prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
printf(" Maximum memory pitch: %u bytes\n", (int)prop.memPitch);
printf(" Texture alignment: %u bytes\n", (int)prop.textureAlignment);
printf(" Concurrent copy and execution: %s with %d copy engine(s)\n", (prop.deviceOverlap ? "Yes" : "No"), prop.asyncEngineCount);
printf(" Run time limit on kernels: %s\n", prop.kernelExecTimeoutEnabled ? "Yes" : "No");
printf(" Integrated GPU sharing Host Memory: %s\n", prop.integrated ? "Yes" : "No");
printf(" Support host page-locked memory mapping: %s\n", prop.canMapHostMemory ? "Yes" : "No");
printf(" Concurrent kernel execution: %s\n", prop.concurrentKernels ? "Yes" : "No");
printf(" Alignment requirement for Surfaces: %s\n", prop.surfaceAlignment ? "Yes" : "No");
printf(" Device has ECC support enabled: %s\n", prop.ECCEnabled ? "Yes" : "No");
printf(" Device is using TCC driver mode: %s\n", prop.tccDriver ? "Yes" : "No");
printf(" Device supports Unified Addressing (UVA): %s\n", prop.unifiedAddressing ? "Yes" : "No");
printf(" Device PCI Bus ID / PCI location ID: %d / %d\n", prop.pciBusID, prop.pciDeviceID );
printf(" Compute Mode:\n");
printf(" %s \n", computeMode[prop.computeMode]);
}
printf("\n");
printf("deviceQuery, CUDA Driver = CUDART");
printf(", CUDA Driver Version = %d.%d", driverVersion / 1000, driverVersion % 100);
printf(", CUDA Runtime Version = %d.%d", runtimeVersion/1000, runtimeVersion%100);
printf(", NumDevs = %d\n\n", count);
fflush(stdout);
}
void cv::gpu::printShortCudaDeviceInfo(int device)
{
int count = getCudaEnabledDeviceCount();
bool valid = (device >= 0) && (device < count);
int beg = valid ? device : 0;
int end = valid ? device+1 : count;
int driverVersion = 0, runtimeVersion = 0;
cudaSafeCall( cudaDriverGetVersion(&driverVersion) );
cudaSafeCall( cudaRuntimeGetVersion(&runtimeVersion) );
for(int dev = beg; dev < end; ++dev)
{
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, dev) );
const char *arch_str = prop.major < 2 ? " (not Fermi)" : "";
printf("Device %d: \"%s\" %.0fMb", dev, prop.name, (float)prop.totalGlobalMem/1048576.0f);
printf(", sm_%d%d%s, %d cores", prop.major, prop.minor, arch_str, convertSMVer2Cores(prop.major, prop.minor) * prop.multiProcessorCount);
printf(", Driver/Runtime ver.%d.%d/%d.%d\n", driverVersion/1000, driverVersion%100, runtimeVersion/1000, runtimeVersion%100);
}
fflush(stdout);
}
#endif