性能：boost.compute vs. opencl C++ 包装器

以下代码分别使用 boost.compute 和 opencl C++ 包装器将两个向量相加。结果显示 boost.compute 几乎比 opencl c++ 包装器慢 20 倍。我想知道我是否错过了使用 boost.compute 或者它确实很慢。 平台：win7、vs2013、boost 1.55、boost.compute 0.2、ATI Radeon HD 4600

代码使用 C++ 包装器：

#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;
}

代码使用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;
 }

生成的内核代码transform() http://kylelutz.github.io/compute/boost/compute/transform.htmlBoost.Compute 中的函数应该与您在 C++ 包装器版本中使用的内核代码几乎相同（尽管 Boost.Compute 会进行一些展开）。

您看到计时差异的原因是，在第一个版本中，您仅测量将内核排队并将结果映射回主机所需的时间。在 Boost.Compute 版本中，您还测量创建transform()内核，编译它，然后执行它。如果您想要更实际的比较，您应该测量第一个示例的总执行时间，包括设置和编译 OpenCL 程序所需的时间。

这种初始化损失（OpenCL 的运行时编译模型所固有的）在 Boost.Compute 中通过在运行时自动缓存已编译的内核（也可以选择离线缓存它们以便在下次运行程序时重用）而得到一定程度的缓解。呼唤transform()第一次调用后多次会快得多。

附：您也可以只使用 Boost.Compute 中的核心包装类（例如device http://kylelutz.github.io/compute/boost/compute/device.html and context http://kylelutz.github.io/compute/boost/compute/context.html）以及容器类（例如vector<T> http://kylelutz.github.io/compute/boost/compute/vector.html）并仍然运行您自己的自定义内核。