Производительность: boost.compute v.s. оболочка opencl c ++

В следующих кодах добавляются два вектора с использованием обертки boost.compute и opencl c ++ соответственно. Результат показывает, что boost.compute почти в 20 раз медленнее, чем оболочка opencl c ++. Интересно, если я пропускаю использовать boost.compute или он действительно медленный.
Платформа: win7, vs2013, boost 1.55, boost.compute 0.2, ATI Radeon HD 4600

Код использует оболочку c ++:

#define __CL_ENABLE_EXCEPTIONS
#include <CL/cl.hpp>
#include <boost/timer/timer.hpp>
#include <boost/smart_ptr/scoped_array.hpp>
#include <fstream>
#include <numeric>
#include <algorithm>
#include <functional>

int main(){
static char kernelSourceCode[] = "\
__kernel void vadd(__global int * a, __global int * b, __global int * c){\
size_t i = get_global_id(0);\
\
c[i] = a[i] + b[i];\
}\
";

using type = boost::scoped_array<int>;
size_t const BUFFER_SIZE = 1UL << 13;
type A(new int[BUFFER_SIZE]);
type B(new int[BUFFER_SIZE]);
type C(new int[BUFFER_SIZE]);

std::iota(A.get(), A.get() + BUFFER_SIZE, 0);
std::transform(A.get(), A.get() + BUFFER_SIZE, B.get(), std::bind(std::multiplies<int>(), std::placeholders::_1, 2));

try {
std::vector<cl::Platform> platformList;
// Pick platform
cl::Platform::get(&platformList);
// Pick first platform
cl_context_properties cprops[] = {
CL_CONTEXT_PLATFORM,
(cl_context_properties)(platformList[0])(),
0
};
cl::Context context(CL_DEVICE_TYPE_GPU, cprops);
// Query the set of devices attached to the context
std::vector<cl::Device> devices = context.getInfo<CL_CONTEXT_DEVICES>();
// Create command-queue
cl::CommandQueue queue(context, devices[0], 0);
// Create the program from source
cl::Program::Sources sources(
1,
std::make_pair(kernelSourceCode, 0)
);
cl::Program program(context, sources);
// Build program
program.build(devices);
// Create buffer for A and copy host contents
cl::Buffer aBuffer = cl::Buffer(
context,
CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
BUFFER_SIZE * sizeof(int),
(void *)&A[0]);
// Create buffer for B and copy host contents
cl::Buffer bBuffer = cl::Buffer(
context,
CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
BUFFER_SIZE * sizeof(int),
(void *)&B[0]);
// Create buffer that uses the host ptr C
cl::Buffer cBuffer = cl::Buffer(
context,
CL_MEM_READ_WRITE | CL_MEM_USE_HOST_PTR,
BUFFER_SIZE * sizeof(int),
(void *)&C[0]);
// Create kernel object
cl::Kernel kernel(program, "vadd");
// Set kernel args
kernel.setArg(0, aBuffer);
kernel.setArg(1, bBuffer);
kernel.setArg(2, cBuffer);
// Do the work
void *output;
{
boost::timer::auto_cpu_timer timer;
queue.enqueueNDRangeKernel(
kernel,
cl::NullRange,
cl::NDRange(BUFFER_SIZE),
cl::NullRange
);
output = (int *)queue.enqueueMapBuffer(
cBuffer,
CL_TRUE, // block
CL_MAP_READ,
0,
BUFFER_SIZE * sizeof(int)
);
}
std::ofstream gpu("gpu.txt");
for (int i = 0; i < BUFFER_SIZE; i++) {
gpu << C[i] << " ";
}
queue.enqueueUnmapMemObject(
cBuffer,
output);
}
catch (cl::Error const &err) {
std::cerr << err.what() << "\n";
}

return EXIT_SUCCESS;
}

Код использует boost.compute:

#include <boost/compute/container/mapped_view.hpp>
#include <boost/compute/algorithm/transform.hpp>
#include <boost/compute/functional/operator.hpp>
#include <numeric>
#include <algorithm>
#include <functional>
#include <boost/timer/timer.hpp>
#include <boost/smart_ptr/scoped_array.hpp>
#include <fstream>
#include <boost/tuple/tuple_comparison.hpp>

int main(){
size_t const BUFFER_SIZE = 1UL << 13;
boost::scoped_array<int> A(new int[BUFFER_SIZE]), B(new int[BUFFER_SIZE]), C(new int[BUFFER_SIZE]);

std::iota(A.get(), A.get() + BUFFER_SIZE, 0);
std::transform(A.get(), A.get() + BUFFER_SIZE, B.get(), std::bind(std::multiplies<int>(), std::placeholders::_1, 2));

try{
if (boost::compute::system::default_device().type() != CL_DEVICE_TYPE_GPU){
std::cerr << "Not GPU\n";
}
else{
boost::compute::command_queue queue = boost::compute::system::default_queue();
boost::compute::mapped_view<int> mA(static_cast<const int*>(A.get()), BUFFER_SIZE),
mB(static_cast<const int*>(B.get()), BUFFER_SIZE);
boost::compute::mapped_view<int> mC(C.get(), BUFFER_SIZE);
{
boost::timer::auto_cpu_timer timer;
boost::compute::transform(
mA.cbegin(), mA.cend(),
mB.cbegin(),
mC.begin(),
boost::compute::plus<int>(),
queue
);
mC.map(CL_MAP_READ, queue);
}
std::ofstream gpu("gpu.txt");
for (size_t i = 0; i != BUFFER_SIZE; ++i) gpu << C[i] << " ";
mC.unmap(queue);
}
}
catch (boost::compute::opencl_error const &err){
std::cerr << err.what() << "\n";
}

return EXIT_SUCCESS;
}