C++ command_queue::get_context方法代码示例

			decltype(auto) matrix_transpose(
					InputRange const& input, OutputRange& output,
					int row_size, int col_size,
					boost::compute::command_queue& queue) {
				NEU_ASSERT(row_size*col_size == range::distance(input));
				static auto transpose_kernel =
					neu::make_kernel(neu::layer::impl::matrix_transpose_kernel_source,
					"matrix_transpose", queue.get_context());
				transpose_kernel.set_args(
					range::get_buffer(input),
					static_cast<cl_int>(range::get_begin_index(input)),
					range::get_buffer(output),
					static_cast<cl_int>(range::get_begin_index(output)),
					static_cast<cl_int>(row_size),
					static_cast<cl_int>(col_size));
				std::size_t global[2] = {
					static_cast<std::size_t>(((col_size-1)/32+1)*32),
					static_cast<std::size_t>(((row_size-1)/32+1)*32)
				};
				std::size_t local[2] = {
					static_cast<std::size_t>(32),
					static_cast<std::size_t>(32)
				};
				queue.enqueue_nd_range_kernel(transpose_kernel, 2, nullptr, global, local);
			}

				static decltype(auto) call(
						std::vector<neu::layer::any_layer>& layers,
						int batch_size,
						InputRange const& initial_input, OutputRange& result_output,
						boost::compute::command_queue& queue) {
					gpu_vector input(initial_input.begin(), initial_input.end(), queue);
					gpu_vector output(queue.get_context());
					int i = 0;
					for(auto& l : layers) {
						output.resize(::neu::layer::output_dim(l)*batch_size, queue);
						/*
						std::cout << "whole" << ::neu::layer::whole_output_size(l) << std::endl;
						std::cout << "i" << i << std::endl;
						std::cout << "aa" << output.size() << std::endl;
						*/
						auto output_range = range::to_range(output);
#ifdef NEU_BENCHMARK_ENABLE
						boost::timer t;
#endif //NEU_BENCHMARK_ENABLE
						l.test_forward(batch_size,
							range::to_range(input), output_range, queue);
#ifdef NEU_BENCHMARK_ENABLE
						queue.finish();
						std::cout << "layer" << i << "\ttest_forward\t" << t.elapsed() << " secs" << std::endl;
#endif //NEU_BENCHMARK_ENABLE
						input.swap(output);
						++i;
					}
					range::copy(input, result_output, queue);
				}

inline void test_fill(T v1, T v2, T v3, bc::command_queue queue) {
    if(boost::is_same<typename bc::scalar_type<T>::type, bc::double_>::value &&
       !queue.get_device().supports_extension("cl_khr_fp64")) {
        std::cerr << "Skipping test_fill<" << bc::type_name<T>() << ">() "
                     "on device which doesn't support cl_khr_fp64" << std::endl;
        return;
    }

    bc::vector<T> vector(4, queue.get_context());
    bc::fill(vector.begin(), vector.end(), v1, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v1, v1, v1, v1));

    vector.resize(1000, queue);
    bc::fill(vector.begin(), vector.end(), v2, queue);
    queue.finish();
    BOOST_CHECK_EQUAL(vector.front(), v2);
    BOOST_CHECK_EQUAL(vector.back(), v2);

    bc::fill(vector.begin() + 500, vector.end(), v3, queue);
    queue.finish();
    BOOST_CHECK_EQUAL(vector.front(), v2);
    BOOST_CHECK_EQUAL(vector[499], v2);
    BOOST_CHECK_EQUAL(vector[500], v3);
    BOOST_CHECK_EQUAL(vector.back(), v3);
}

inline void test_fill_n(T v1, T v2, T v3, bc::command_queue queue) {
    bc::vector<T> vector(4, queue.get_context());
    bc::fill_n(vector.begin(), 4, v1, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v1, v1, v1, v1));

    bc::fill_n(vector.begin(), 3, v2, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v2, v2, v2, v1));

    bc::fill_n(vector.begin() + 1, 2, v3, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v2, v3, v3, v1));

    bc::fill_n(vector.begin(), 4, v2, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v2, v2, v2, v2));

    // fill last element
    bc::fill_n(vector.end() - 1, 1, v3, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v2, v2, v2, v3));

    // fill first element
    bc::fill_n(vector.begin(), 1, v1, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v1, v2, v2, v3));
}

inline void test_fill_n(T v1, T v2, T v3, bc::command_queue queue) {
    if(boost::is_same<typename bc::scalar_type<T>::type, bc::double_>::value &&
       !queue.get_device().supports_extension("cl_khr_fp64")) {
        std::cerr << "Skipping test_fill_n<" << bc::type_name<T>() << ">() "
                     "on device which doesn't support cl_khr_fp64" << std::endl;
        return;
    }

    bc::vector<T> vector(4, queue.get_context());
    bc::fill_n(vector.begin(), 4, v1, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v1, v1, v1, v1));

    bc::fill_n(vector.begin(), 3, v2, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v2, v2, v2, v1));

    bc::fill_n(vector.begin() + 1, 2, v3, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v2, v3, v3, v1));

    bc::fill_n(vector.begin(), 4, v2, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v2, v2, v2, v2));

    // fill last element
    bc::fill_n(vector.end() - 1, 1, v3, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v2, v2, v2, v3));

    // fill first element
    bc::fill_n(vector.begin(), 1, v1, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v1, v2, v2, v3));
}

void perf_random_number_engine(const size_t size,
                               const size_t trials,
                               compute::command_queue& queue)
{
    typedef typename Engine::result_type T;

    // create random number engine
    Engine engine(queue);

    // create vector on the device
    std::cout << "size = " << size << std::endl;
    compute::vector<T> vector(size, queue.get_context());

    // generate random numbers
    perf_timer t;
    for(size_t i = 0; i < trials; i++){
        t.start();
        engine.generate(vector.begin(), vector.end(), queue);
        queue.finish();
        t.stop();
    }

    // print result
    std::cout << "time: " << t.min_time() / 1e6 << " ms" << std::endl;
    std::cout << "rate: " << perf_rate<T>(size, t.min_time()) << " MB/s" << std::endl;
}

        device_vector(const boost::compute::command_queue &q, size_t n,
                const T *host = 0, mem_flags flags = MEM_READ_WRITE)
        {
            if (host && !(flags & CL_MEM_USE_HOST_PTR))
                flags |= CL_MEM_COPY_HOST_PTR;

            if (n)
                buffer = boost::compute::buffer(q.get_context(), n * sizeof(T),
                        flags, static_cast<void*>(const_cast<T*>(host)));
        }

    void generate(OutputIterator first, OutputIterator last, Function op, boost::compute::command_queue &queue)
    {
        boost::compute::vector<T> tmp(std::distance(first, last), queue.get_context());

        BOOST_COMPUTE_FUNCTION(T, max_random, (const T x),
        {
            if(get_global_id(0) < 1)
                return (ValueType) MAX_RANDOM;
            else
                return (ValueType) 0;
        });

void test_copy_if_odd(compute::command_queue &queue)
{
    // create input and output vectors on the device
    const compute::context &context = queue.get_context();
    compute::vector<int> input(PERF_N, context);
    compute::vector<int> output(PERF_N, context);

    // generate random numbers between 1 and 10
    compute::default_random_engine rng(queue);
    compute::uniform_int_distribution<int> d(1, 10);
    d.generate(input.begin(), input.end(), rng, queue);

    BOOST_COMPUTE_FUNCTION(bool, is_odd, (int x),
    {
        return x & 1;
    });

// tesselates a sphere with radius, phi_slices, and theta_slices. returns
// a shared opencl/opengl buffer containing the vertex data.
compute::opengl_buffer tesselate_sphere(float radius,
                                        size_t phi_slices,
                                        size_t theta_slices,
                                        compute::command_queue &queue)
{
    using compute::dim;

    const compute::context &context = queue.get_context();

    const size_t vertex_count = phi_slices * theta_slices;

    // create opengl buffer
    GLuint vbo;
    vtkgl::GenBuffersARB(1, &vbo);
    vtkgl::BindBufferARB(vtkgl::ARRAY_BUFFER, vbo);
    vtkgl::BufferDataARB(vtkgl::ARRAY_BUFFER,
                         sizeof(float) * 4 * vertex_count,
                         NULL,
                         vtkgl::STREAM_DRAW);
    vtkgl::BindBufferARB(vtkgl::ARRAY_BUFFER, 0);

    // create shared opengl/opencl buffer
    compute::opengl_buffer vertex_buffer(context, vbo);

    // tesselate_sphere kernel source
    const char source[] = BOOST_COMPUTE_STRINGIZE_SOURCE(
        __kernel void tesselate_sphere(float radius,
                                       uint phi_slices,
                                       uint theta_slices,
                                       __global float4 *vertex_buffer)
        {
            const uint phi_i = get_global_id(0);
            const uint theta_i = get_global_id(1);

            const float phi = phi_i * 2.f * M_PI_F / phi_slices;
            const float theta = theta_i * 2.f * M_PI_F / theta_slices;

            float4 v;
            v.x = radius * cos(theta) * cos(phi);
            v.y = radius * cos(theta) * sin(phi);
            v.z = radius * sin(theta);
            v.w = 1.f;

            vertex_buffer[phi_i*phi_slices+theta_i] = v;
        }
    );

/**
 * If VEXCL_CACHE_KERNELS macro is defined, then program binaries are cached
 * in filesystem and reused in the following runs.
 */
inline boost::compute::program build_sources(
    const boost::compute::command_queue &queue,
    const std::string &source,
    const std::string &options = ""
)
{
#ifdef VEXCL_SHOW_KERNELS
    std::cout << source << std::endl;
#else
    if (getenv("VEXCL_SHOW_KERNELS"))
        std::cout << source << std::endl;
#endif

    return boost::compute::program::build_with_source(
               source, queue.get_context(),
               options + " " + get_compile_options(queue)
           );
}

inline void test_fill(T v1, T v2, T v3, bc::command_queue queue) {
    bc::vector<T> vector(4, queue.get_context());
    bc::fill(vector.begin(), vector.end(), v1, queue);
    queue.finish();
    CHECK_RANGE_EQUAL(T, 4, vector, (v1, v1, v1, v1));

    vector.resize(1000, queue);
    bc::fill(vector.begin(), vector.end(), v2, queue);
    queue.finish();
    BOOST_CHECK_EQUAL(vector.front(), v2);
    BOOST_CHECK_EQUAL(vector.back(), v2);

    bc::fill(vector.begin() + 500, vector.end(), v3, queue);
    queue.finish();
    BOOST_CHECK_EQUAL(vector.front(), v2);
    BOOST_CHECK_EQUAL(vector[499], v2);
    BOOST_CHECK_EQUAL(vector[500], v3);
    BOOST_CHECK_EQUAL(vector.back(), v3);
}

inline void box_filter_image(const compute::image2d &input,
                             compute::image2d &output,
                             compute::uint_ box_height,
                             compute::uint_ box_width,
                             compute::command_queue &queue)
{
    using compute::dim;

    const compute::context &context = queue.get_context();

    // simple box filter kernel source
    const char source[] = BOOST_COMPUTE_STRINGIZE_SOURCE(
        __kernel void box_filter(__read_only image2d_t input,
                                 __write_only image2d_t output,
                                 uint box_height,
                                 uint box_width)
        {
            int x = get_global_id(0);
            int y = get_global_id(1);
            int h = get_image_height(input);
            int w = get_image_width(input);
            int k = box_width;
            int l = box_height;

            if(x < k/2 || y < l/2 || x >= w-(k/2) || y >= h-(l/2)){
                write_imagef(output, (int2)(x, y), (float4)(0, 0, 0, 1));
            }
            else {
                const sampler_t sampler = CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;

                float4 sum = { 0, 0, 0, 0 };
                for(int i = 0; i < k; i++){
                    for(int j = 0; j < l; j++){
                        sum += read_imagef(input, sampler, (int2)(x+i-k, y+j-l));
                    }
                }
                sum /= (float) k * l;
                float4 value = (float4)( sum.x, sum.y, sum.z, 1.f );
                write_imagef(output, (int2)(x, y), value);
            }
        }

	vector(vector_expression<E, gpu_tag> const& e, boost::compute::command_queue& queue)
	: m_storage(e().size(), queue.get_context())
	, m_queue(&queue){
		assign(*this, e);
	}

	/// \brief Constructor of a vector with a predefined size
	/// By default, its elements are uninitialized.
	/// \param size initial size of the vector
	/// \param queue the opencl queue to use by this vector
	explicit vector(size_type size, boost::compute::command_queue& queue = boost::compute::system::default_queue())
	: m_storage(size,queue.get_context()), m_queue(&queue){}