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CUDA related func tables refactored to remove unneeded dependencies.

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pull/2022/head
Alexander Smorkalov 11 years ago
parent 6da7c50fb5
commit 64c94cb22c
2 changed files with 204 additions and 210 deletions
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  1. 30
      modules/core/src/gpumat.cpp
  2. 384
      modules/core/src/gpumat_cuda.hpp

30
modules/core/src/gpumat.cpp
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@ -239,23 +239,23 @@ static DeviceInfoFuncTable* deviceInfoFuncTable()
//////////////////////////////// Initialization & Info ////////////////////////
int cv::gpu::getCudaEnabledDeviceCount() { return gpuFuncTable()->getCudaEnabledDeviceCount(); }
int cv::gpu::getCudaEnabledDeviceCount() { return deviceInfoFuncTable()->getCudaEnabledDeviceCount(); }
void cv::gpu::setDevice(int device) { gpuFuncTable()->setDevice(device); }
int cv::gpu::getDevice() { return gpuFuncTable()->getDevice(); }
void cv::gpu::setDevice(int device) { deviceInfoFuncTable()->setDevice(device); }
int cv::gpu::getDevice() { return deviceInfoFuncTable()->getDevice(); }
void cv::gpu::resetDevice() { gpuFuncTable()->resetDevice(); }
void cv::gpu::resetDevice() { deviceInfoFuncTable()->resetDevice(); }
bool cv::gpu::deviceSupports(FeatureSet feature_set) { return gpuFuncTable()->deviceSupports(feature_set); }
bool cv::gpu::deviceSupports(FeatureSet feature_set) { return deviceInfoFuncTable()->deviceSupports(feature_set); }
bool cv::gpu::TargetArchs::builtWith(FeatureSet feature_set) { return gpuFuncTable()->builtWith(feature_set); }
bool cv::gpu::TargetArchs::has(int major, int minor) { return gpuFuncTable()->has(major, minor); }
bool cv::gpu::TargetArchs::hasPtx(int major, int minor) { return gpuFuncTable()->hasPtx(major, minor); }
bool cv::gpu::TargetArchs::hasBin(int major, int minor) { return gpuFuncTable()->hasBin(major, minor); }
bool cv::gpu::TargetArchs::hasEqualOrLessPtx(int major, int minor) { return gpuFuncTable()->hasEqualOrLessPtx(major, minor); }
bool cv::gpu::TargetArchs::hasEqualOrGreater(int major, int minor) { return gpuFuncTable()->hasEqualOrGreater(major, minor); }
bool cv::gpu::TargetArchs::hasEqualOrGreaterPtx(int major, int minor) { return gpuFuncTable()->hasEqualOrGreaterPtx(major, minor); }
bool cv::gpu::TargetArchs::hasEqualOrGreaterBin(int major, int minor) { return gpuFuncTable()->hasEqualOrGreaterBin(major, minor); }
bool cv::gpu::TargetArchs::builtWith(FeatureSet feature_set) { return deviceInfoFuncTable()->builtWith(feature_set); }
bool cv::gpu::TargetArchs::has(int major, int minor) { return deviceInfoFuncTable()->has(major, minor); }
bool cv::gpu::TargetArchs::hasPtx(int major, int minor) { return deviceInfoFuncTable()->hasPtx(major, minor); }
bool cv::gpu::TargetArchs::hasBin(int major, int minor) { return deviceInfoFuncTable()->hasBin(major, minor); }
bool cv::gpu::TargetArchs::hasEqualOrLessPtx(int major, int minor) { return deviceInfoFuncTable()->hasEqualOrLessPtx(major, minor); }
bool cv::gpu::TargetArchs::hasEqualOrGreater(int major, int minor) { return deviceInfoFuncTable()->hasEqualOrGreater(major, minor); }
bool cv::gpu::TargetArchs::hasEqualOrGreaterPtx(int major, int minor) { return deviceInfoFuncTable()->hasEqualOrGreaterPtx(major, minor); }
bool cv::gpu::TargetArchs::hasEqualOrGreaterBin(int major, int minor) { return deviceInfoFuncTable()->hasEqualOrGreaterBin(major, minor); }
size_t cv::gpu::DeviceInfo::sharedMemPerBlock() const { return deviceInfoFuncTable()->sharedMemPerBlock(); }
void cv::gpu::DeviceInfo::queryMemory(size_t& total_memory, size_t& free_memory) const { deviceInfoFuncTable()->queryMemory(total_memory, free_memory); }
@ -270,8 +270,8 @@ std::string cv::gpu::DeviceInfo::name() const { return deviceInfoFuncTable()->na
int cv::gpu::DeviceInfo::multiProcessorCount() const { return deviceInfoFuncTable()->multiProcessorCount(); }
void cv::gpu::DeviceInfo::query() { deviceInfoFuncTable()->query(); }
void cv::gpu::printCudaDeviceInfo(int device) { gpuFuncTable()->printCudaDeviceInfo(device); }
void cv::gpu::printShortCudaDeviceInfo(int device) { gpuFuncTable()->printShortCudaDeviceInfo(device); }
void cv::gpu::printCudaDeviceInfo(int device) { deviceInfoFuncTable()->printCudaDeviceInfo(device); }
void cv::gpu::printShortCudaDeviceInfo(int device) { deviceInfoFuncTable()->printShortCudaDeviceInfo(device); }
#ifdef HAVE_CUDA

384
modules/core/src/gpumat_cuda.hpp
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@ -4,6 +4,7 @@
class DeviceInfoFuncTable
{
public:
// cv::DeviceInfo
virtual size_t sharedMemPerBlock() const = 0;
virtual void queryMemory(size_t&, size_t&) const = 0;
virtual size_t freeMemory() const = 0;
@ -16,25 +17,13 @@
virtual int majorVersion() const = 0;
virtual int minorVersion() const = 0;
virtual int multiProcessorCount() const = 0;
virtual ~DeviceInfoFuncTable() {};
};
class GpuFuncTable
{
public:
virtual ~GpuFuncTable() {}
// DeviceInfo routines
virtual int getCudaEnabledDeviceCount() const = 0;
virtual void setDevice(int) const = 0;
virtual int getDevice() const = 0;
virtual void resetDevice() const = 0;
virtual bool deviceSupports(FeatureSet) const = 0;
// TargetArchs
// cv::TargetArchs
virtual bool builtWith(FeatureSet) const = 0;
virtual bool has(int, int) const = 0;
virtual bool hasPtx(int, int) const = 0;
@ -46,7 +35,15 @@
virtual void printCudaDeviceInfo(int) const = 0;
virtual void printShortCudaDeviceInfo(int) const = 0;
virtual ~DeviceInfoFuncTable() {};
};
class GpuFuncTable
{
public:
virtual ~GpuFuncTable() {}
// GpuMat routines
virtual void copy(const Mat& src, GpuMat& dst) const = 0;
virtual void copy(const GpuMat& src, Mat& dst) const = 0;
@ -60,7 +57,7 @@
// for gpu::device::setTo funcs
virtual void setTo(cv::gpu::GpuMat&, cv::Scalar, const cv::gpu::GpuMat&, CUstream_st*) const = 0;
virtual void mallocPitch(void** devPtr, size_t* step, size_t width, size_t height) const = 0;
virtual void free(void* devPtr) const = 0;
};
@ -80,20 +77,14 @@
int majorVersion() const { throw_nogpu; return -1; }
int minorVersion() const { throw_nogpu; return -1; }
int multiProcessorCount() const { throw_nogpu; return -1; }
};
class EmptyFuncTable : public GpuFuncTable
{
public:
// DeviceInfo routines
int getCudaEnabledDeviceCount() const { return 0; }
void setDevice(int) const { throw_nogpu; }
int getDevice() const { throw_nogpu; return 0; }
void resetDevice() const { throw_nogpu; }
bool deviceSupports(FeatureSet) const { throw_nogpu; return false; }
bool builtWith(FeatureSet) const { throw_nogpu; return false; }
@ -104,10 +95,15 @@
bool hasEqualOrGreater(int, int) const { throw_nogpu; return false; }
bool hasEqualOrGreaterPtx(int, int) const { throw_nogpu; return false; }
bool hasEqualOrGreaterBin(int, int) const { throw_nogpu; return false; }
void printCudaDeviceInfo(int) const { throw_nogpu; }
void printShortCudaDeviceInfo(int) const { throw_nogpu; }
};
class EmptyFuncTable : public GpuFuncTable
{
public:
void copy(const Mat&, GpuMat&) const { throw_nogpu; }
void copy(const GpuMat&, Mat&) const { throw_nogpu; }
void copy(const GpuMat&, GpuMat&) const { throw_nogpu; }
@ -185,62 +181,62 @@ namespace cv { namespace gpu { namespace device
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI);
};
template<int DDEPTH> struct NppConvertFunc<CV_32F, DDEPTH>
{
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
typedef NppStatus (*func_ptr)(const Npp32f* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI, NppRoundMode eRoundMode);
};
template<int SDEPTH, int DDEPTH, typename NppConvertFunc<SDEPTH, DDEPTH>::func_ptr func> struct NppCvt
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
static void call(const GpuMat& src, GpuMat& dst)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int DDEPTH, typename NppConvertFunc<CV_32F, DDEPTH>::func_ptr func> struct NppCvt<CV_32F, DDEPTH, func>
{
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t;
static void call(const GpuMat& src, GpuMat& dst)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz, NPP_RND_NEAR) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
//////////////////////////////////////////////////////////////////////////
// Set
template<int SDEPTH, int SCN> struct NppSetFunc
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI);
};
template<int SDEPTH> struct NppSetFunc<SDEPTH, 1>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI);
};
template<int SCN> struct NppSetFunc<CV_8S, SCN>
@ -251,172 +247,172 @@ namespace cv { namespace gpu { namespace device
{
typedef NppStatus (*func_ptr)(Npp8s val, Npp8s* pSrc, int nSrcStep, NppiSize oSizeROI);
};
template<int SDEPTH, int SCN, typename NppSetFunc<SDEPTH, SCN>::func_ptr func> struct NppSet
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int SDEPTH, typename NppSetFunc<SDEPTH, 1>::func_ptr func> struct NppSet<SDEPTH, 1, func>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int SDEPTH, int SCN> struct NppSetMaskFunc
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
};
template<int SDEPTH> struct NppSetMaskFunc<SDEPTH, 1>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
};
template<int SDEPTH, int SCN, typename NppSetMaskFunc<SDEPTH, SCN>::func_ptr func> struct NppSetMask
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s, const GpuMat& mask)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int SDEPTH, typename NppSetMaskFunc<SDEPTH, 1>::func_ptr func> struct NppSetMask<SDEPTH, 1, func>
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(GpuMat& src, Scalar s, const GpuMat& mask)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
//////////////////////////////////////////////////////////////////////////
// CopyMasked
template<int SDEPTH> struct NppCopyMaskedFunc
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, src_t* pDst, int nDstStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
};
template<int SDEPTH, typename NppCopyMaskedFunc<SDEPTH>::func_ptr func> struct NppCopyMasked
{
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
static void call(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t /*stream*/)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<src_t>(), static_cast<int>(dst.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template <typename T> static inline bool isAligned(const T* ptr, size_t size)
{
return reinterpret_cast<size_t>(ptr) % size == 0;
}
namespace cv { namespace gpu { namespace device
{
void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream = 0)
{
CV_Assert(src.size() == dst.size() && src.type() == dst.type());
CV_Assert(src.size() == mask.size() && mask.depth() == CV_8U && (mask.channels() == 1 || mask.channels() == src.channels()));
cv::gpu::device::copyToWithMask_gpu(src.reshape(1), dst.reshape(1), src.elemSize1(), src.channels(), mask.reshape(1), mask.channels() != 1, stream);
}
void convertTo(const GpuMat& src, GpuMat& dst)
{
cv::gpu::device::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), 1.0, 0.0, 0);
}
void convertTo(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream = 0)
{
cv::gpu::device::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), alpha, beta, stream);
}
void setTo(GpuMat& src, Scalar s, cudaStream_t stream)
{
typedef void (*caller_t)(GpuMat& src, Scalar s, cudaStream_t stream);
static const caller_t callers[] =
{
kernelSetCaller<uchar>, kernelSetCaller<schar>, kernelSetCaller<ushort>, kernelSetCaller<short>, kernelSetCaller<int>,
kernelSetCaller<float>, kernelSetCaller<double>
};
callers[src.depth()](src, s, stream);
}
void setTo(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream)
{
typedef void (*caller_t)(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream);
static const caller_t callers[] =
{
kernelSetCaller<uchar>, kernelSetCaller<schar>, kernelSetCaller<ushort>, kernelSetCaller<short>, kernelSetCaller<int>,
kernelSetCaller<float>, kernelSetCaller<double>
};
callers[src.depth()](src, s, mask, stream);
}
void setTo(GpuMat& src, Scalar s)
{
setTo(src, s, 0);
}
void setTo(GpuMat& src, Scalar s, const GpuMat& mask)
{
setTo(src, s, mask, 0);
@ -433,56 +429,56 @@ namespace cv { namespace gpu { namespace device
fromStr(CUDA_ARCH_PTX, ptx);
fromStr(CUDA_ARCH_FEATURES, features);
}
bool builtWith(FeatureSet feature_set) const
{
return !features.empty() && (features.back() >= feature_set);
}
bool hasPtx(int major, int minor) const
{
return find(ptx.begin(), ptx.end(), major * 10 + minor) != ptx.end();
}
bool hasBin(int major, int minor) const
{
return find(bin.begin(), bin.end(), major * 10 + minor) != bin.end();
}
bool hasEqualOrLessPtx(int major, int minor) const
{
return !ptx.empty() && (ptx.front() <= major * 10 + minor);
}
bool hasEqualOrGreaterPtx(int major, int minor) const
{
return !ptx.empty() && (ptx.back() >= major * 10 + minor);
}
bool hasEqualOrGreaterBin(int major, int minor) const
{
return !bin.empty() && (bin.back() >= major * 10 + minor);
}
private:
void fromStr(const string& set_as_str, vector<int>& arr)
{
if (set_as_str.find_first_not_of(" ") == string::npos)
return;
istringstream stream(set_as_str);
int cur_value;
while (!stream.eof())
{
stream >> cur_value;
arr.push_back(cur_value);
}
sort(arr.begin(), arr.end());
}
vector<int> bin;
vector<int> ptx;
vector<int> features;
@ -495,7 +491,7 @@ namespace cv { namespace gpu { namespace device
{
props_.resize(10, 0);
}
~DeviceProps()
{
for (size_t i = 0; i < props_.size(); ++i)
@ -505,18 +501,18 @@ namespace cv { namespace gpu { namespace device
}
props_.clear();
}
cudaDeviceProp* get(int devID)
{
if (devID >= (int) props_.size())
props_.resize(devID + 5, 0);
if (!props_[devID])
{
props_[devID] = new cudaDeviceProp;
cudaSafeCall( cudaGetDeviceProperties(props_[devID], devID) );
}
return props_[devID];
}
private:
@ -524,7 +520,7 @@ namespace cv { namespace gpu { namespace device
};
DeviceProps deviceProps;
class CudaDeviceInfoFuncTable: DeviceInfoFuncTable
{
public:
@ -532,57 +528,57 @@ namespace cv { namespace gpu { namespace device
{
return deviceProps.get(device_id_)->sharedMemPerBlock;
}
void queryMemory(size_t& _totalMemory, size_t& _freeMemory) const
{
int prevDeviceID = getDevice();
if (prevDeviceID != device_id_)
setDevice(device_id_);
cudaSafeCall( cudaMemGetInfo(&_freeMemory, &_totalMemory) );
if (prevDeviceID != device_id_)
setDevice(prevDeviceID);
}
size_t freeMemory() const
{
size_t _totalMemory, _freeMemory;
queryMemory(_totalMemory, _freeMemory);
return _freeMemory;
}
size_t totalMemory() const
{
size_t _totalMemory, _freeMemory;
queryMemory(_totalMemory, _freeMemory);
return _totalMemory;
}
bool supports(FeatureSet feature_set) const
{
int version = majorVersion_ * 10 + minorVersion_;
return version >= feature_set;
}
bool isCompatible() const
{
// Check PTX compatibility
if (TargetArchs::hasEqualOrLessPtx(majorVersion_, minorVersion_))
if (hasEqualOrLessPtx(majorVersion_, minorVersion_))
return true;
// Check BIN compatibility
for (int i = minorVersion_; i >= 0; --i)
if (TargetArchs::hasBin(majorVersion_, i))
if (hasBin(majorVersion_, i))
return true;
return false;
}
void query()
{
const cudaDeviceProp* prop = deviceProps.get(device_id_);
name_ = prop->name;
multi_processor_count_ = prop->multiProcessorCount;
majorVersion_ = prop->major;
@ -614,116 +610,78 @@ namespace cv { namespace gpu { namespace device
return multi_processor_count_;
}
private:
int device_id_;
std::string name_;
int multi_processor_count_;
int majorVersion_;
int minorVersion_;
};
class CudaFuncTable : public GpuFuncTable
{
protected:
const CudaArch cudaArch;
int convertSMVer2Cores(int major, int minor) const
{
// 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 }, {0x30, 192}, {0x35, 192}, { -1, -1 } };
int index = 0;
while (gpuArchCoresPerSM[index].SM != -1)
{
if (gpuArchCoresPerSM[index].SM == ((major << 4) + minor) )
return gpuArchCoresPerSM[index].Cores;
index++;
}
return -1;
}
public:
int getCudaEnabledDeviceCount() const
{
int count;
cudaError_t error = cudaGetDeviceCount( &count );
if (error == cudaErrorInsufficientDriver)
return -1;
if (error == cudaErrorNoDevice)
return 0;
cudaSafeCall( error );
return count;
}
void setDevice(int device) const
{
cudaSafeCall( cudaSetDevice( device ) );
}
int getDevice() const
{
int device;
cudaSafeCall( cudaGetDevice( &device ) );
return device;
}
void resetDevice() const
{
cudaSafeCall( cudaDeviceReset() );
}
bool builtWith(FeatureSet feature_set) const
{
return cudaArch.builtWith(feature_set);
}
bool has(int major, int minor) const
{
return hasPtx(major, minor) || hasBin(major, minor);
}
bool hasPtx(int major, int minor) const
{
return cudaArch.hasPtx(major, minor);
}
bool hasBin(int major, int minor) const
{
return cudaArch.hasBin(major, minor);
}
bool hasEqualOrLessPtx(int major, int minor) const
{
return cudaArch.hasEqualOrLessPtx(major, minor);
}
bool hasEqualOrGreater(int major, int minor) const
{
return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor);
}
bool hasEqualOrGreaterPtx(int major, int minor) const
{
return cudaArch.hasEqualOrGreaterPtx(major, minor);
}
bool hasEqualOrGreaterBin(int major, int minor) const
{
return cudaArch.hasEqualOrGreaterBin(major, minor);
}
bool deviceSupports(FeatureSet feature_set) const
{
static int versions[] =
@ -731,11 +689,11 @@ namespace cv { namespace gpu { namespace device
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
};
static const int cache_size = static_cast<int>(sizeof(versions) / sizeof(versions[0]));
const int devId = getDevice();
int version;
if (devId < cache_size && versions[devId] >= 0)
version = versions[devId];
else
@ -745,25 +703,25 @@ namespace cv { namespace gpu { namespace device
if (devId < cache_size)
versions[devId] = version;
}
return TargetArchs::builtWith(feature_set) && (version >= feature_set);
}
void printCudaDeviceInfo(int device) const
{
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)",
@ -772,30 +730,30 @@ namespace cv { namespace gpu { namespace 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);
int cores = convertSMVer2Cores(prop.major, prop.minor);
if (cores > 0)
printf(" (%2d) Multiprocessors x (%2d) CUDA Cores/MP: %d CUDA Cores\n", prop.multiProcessorCount, cores, cores * prop.multiProcessorCount);
printf(" GPU Clock Speed: %.2f GHz\n", prop.clockRate * 1e-6f);
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]);
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]);
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);
@ -805,12 +763,12 @@ namespace cv { namespace gpu { namespace device
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");
@ -820,7 +778,7 @@ namespace cv { namespace gpu { namespace device
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);
@ -828,37 +786,73 @@ namespace cv { namespace gpu { namespace device
printf(", NumDevs = %d\n\n", count);
fflush(stdout);
}
void printShortCudaDeviceInfo(int device) const
{
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", prop.major, prop.minor, arch_str);
int cores = convertSMVer2Cores(prop.major, prop.minor);
if (cores > 0)
printf(", %d cores", cores * prop.multiProcessorCount);
printf(", Driver/Runtime ver.%d.%d/%d.%d\n", driverVersion/1000, driverVersion%100, runtimeVersion/1000, runtimeVersion%100);
}
fflush(stdout);
}
private:
int device_id_;
std::string name_;
int multi_processor_count_;
int majorVersion_;
int minorVersion_;
const CudaArch cudaArch;
int convertSMVer2Cores(int major, int minor) const
{
// 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 }, {0x30, 192}, {0x35, 192}, { -1, -1 } };
int index = 0;
while (gpuArchCoresPerSM[index].SM != -1)
{
if (gpuArchCoresPerSM[index].SM == ((major << 4) + minor) )
return gpuArchCoresPerSM[index].Cores;
index++;
}
return -1;
}
};
class CudaFuncTable : public GpuFuncTable
{
public:
void copy(const Mat& src, GpuMat& dst) const
{
cudaSafeCall( cudaMemcpy2D(dst.data, dst.step, src.data, src.step, src.cols * src.elemSize(), src.rows, cudaMemcpyHostToDevice) );

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