Example #1
0
inline size_t bitonic_block_sort(KeyIterator keys_first,
                                 ValueIterator values_first,
                                 Compare compare,
                                 const size_t count,
                                 const bool sort_by_key,
                                 command_queue &queue)
{
    typedef typename std::iterator_traits<KeyIterator>::value_type key_type;
    typedef typename std::iterator_traits<ValueIterator>::value_type value_type;

    meta_kernel k("bitonic_block_sort");
    size_t count_arg = k.add_arg<const uint_>("count");

    size_t local_keys_arg = k.add_arg<key_type *>(memory_object::local_memory, "lkeys");
    size_t local_vals_arg = 0;
    if(sort_by_key) {
        local_vals_arg = k.add_arg<uchar_ *>(memory_object::local_memory, "lidx");
    }

    k <<
        // Work item global and local ids
        k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
        k.decl<const uint_>("lid") << " = get_local_id(0);\n";

    // declare my_key and my_value
    k <<
        k.decl<key_type>("my_key") << ";\n";
    // Instead of copying values (my_value) in local memory with keys
    // we save local index (uchar) and copy my_value at the end at
    // final index. This saves local memory.
    if(sort_by_key)
    {
        k <<
            k.decl<uchar_>("my_index") << " = (uchar)(lid);\n";
    }

    // load key
    k <<
        "if(gid < count) {\n" <<
            k.var<key_type>("my_key") <<  " = " <<
                keys_first[k.var<const uint_>("gid")] << ";\n" <<
        "}\n";

    // load key and index to local memory
    k <<
        "lkeys[lid] = my_key;\n";
    if(sort_by_key)
    {
        k <<
            "lidx[lid] = my_index;\n";
    }
    k <<
        k.decl<const uint_>("offset") << " = get_group_id(0) * get_local_size(0);\n" <<
        k.decl<const uint_>("n") << " = min((uint)(get_local_size(0)),(count - offset));\n";

    // When work group size is a power of 2 bitonic sorter can be used;
    // otherwise, slower odd-even sort is used.

    k <<
        // check if n is power of 2
        "if(((n != 0) && ((n & (~n + 1)) == n))) {\n";

    // bitonic sort, not stable
    k <<
        // wait for keys and vals to be stored in local memory
        "barrier(CLK_LOCAL_MEM_FENCE);\n" <<

        "#pragma unroll\n" <<
        "for(" <<
            k.decl<uint_>("length") << " = 1; " <<
            "length < n; " <<
            "length <<= 1" <<
        ") {\n" <<
            // direction of sort: false -> asc, true -> desc
            k.decl<bool>("direction") << "= ((lid & (length<<1)) != 0);\n" <<
            "for(" <<
                k.decl<uint_>("k") << " = length; " <<
                "k > 0; " <<
                "k >>= 1" <<
            ") {\n" <<

            // sibling to compare with my key
            k.decl<uint_>("sibling_idx") << " = lid ^ k;\n" <<
            k.decl<key_type>("sibling_key") << " = lkeys[sibling_idx];\n" <<
            k.decl<bool>("compare") << " = " <<
                compare(k.var<key_type>("sibling_key"),
                        k.var<key_type>("my_key")) << ";\n" <<
            k.decl<bool>("equal") << " = !(compare || " <<
                compare(k.var<key_type>("my_key"),
                        k.var<key_type>("sibling_key")) << ");\n" <<
            k.decl<bool>("swap") <<
                " = compare ^ (sibling_idx < lid) ^ direction;\n" <<
            "swap = equal ? false : swap;\n" <<
            "my_key = swap ? sibling_key : my_key;\n";
    if(sort_by_key)
    {
        k <<
            "my_index = swap ? lidx[sibling_idx] : my_index;\n";
    }
    k <<
            "barrier(CLK_LOCAL_MEM_FENCE);\n" <<
            "lkeys[lid] = my_key;\n";
    if(sort_by_key)
    {
        k <<
            "lidx[lid] = my_index;\n";
    }
    k <<
            "barrier(CLK_LOCAL_MEM_FENCE);\n" <<
            "}\n" <<
         "}\n";

    // end of bitonic sort

    // odd-even sort, not stable
    k <<
        "}\n" <<
        "else { \n";

    k <<
        k.decl<bool>("lid_is_even") << " = (lid%2) == 0;\n" <<
        k.decl<uint_>("oddsibling_idx") << " = " <<
            "(lid_is_even) ? max(lid,(uint)(1)) - 1 : min(lid+1,n-1);\n" <<
        k.decl<uint_>("evensibling_idx") << " = " <<
            "(lid_is_even) ? min(lid+1,n-1) : max(lid,(uint)(1)) - 1;\n" <<

        // wait for keys and vals to be stored in local memory
        "barrier(CLK_LOCAL_MEM_FENCE);\n" <<

        "#pragma unroll\n" <<
        "for(" <<
            k.decl<uint_>("i") << " = 0; " <<
            "i < n; " <<
            "i++" <<
        ") {\n" <<
            k.decl<uint_>("sibling_idx") <<
                " = i%2 == 0 ? evensibling_idx : oddsibling_idx;\n" <<
            k.decl<key_type>("sibling_key") << " = lkeys[sibling_idx];\n" <<
            k.decl<bool>("compare") << " = " <<
                compare(k.var<key_type>("sibling_key"),
                        k.var<key_type>("my_key")) << ";\n" <<
            k.decl<bool>("equal") << " = !(compare || " <<
                compare(k.var<key_type>("my_key"),
                        k.var<key_type>("sibling_key")) << ");\n" <<
            k.decl<bool>("swap") <<
                " = compare ^ (sibling_idx < lid);\n" <<
            "swap = equal ? false : swap;\n" <<
            "my_key = swap ? sibling_key : my_key;\n";
    if(sort_by_key)
    {
        k <<
            "my_index = swap ? lidx[sibling_idx] : my_index;\n";
    }
    k <<
            "barrier(CLK_LOCAL_MEM_FENCE);\n" <<
            "lkeys[lid] = my_key;\n";
    if(sort_by_key)
    {
        k <<
            "lidx[lid] = my_index;\n";
    }
    k <<
            "barrier(CLK_LOCAL_MEM_FENCE);\n"
        "}\n" <<  // for

    "}\n"; // else
    // end of odd-even sort

    // save key and value
    k <<
        "if(gid < count) {\n" <<
        keys_first[k.var<const uint_>("gid")] << " = " <<
            k.var<key_type>("my_key") << ";\n";
    if(sort_by_key)
    {
        k <<
            k.decl<value_type>("my_value") << " = " <<
                values_first[k.var<const uint_>("offset + my_index")] << ";\n" <<
            "barrier(CLK_GLOBAL_MEM_FENCE);\n" <<
            values_first[k.var<const uint_>("gid")] << " = my_value;\n";
    }
    k <<
        // end if
        "}\n";

    const context &context = queue.get_context();
    const device &device = queue.get_device();
    ::boost::compute::kernel kernel = k.compile(context);

    const size_t work_group_size =
        pick_bitonic_block_sort_block_size<key_type, uchar_>(
            kernel.get_work_group_info<size_t>(
                device, CL_KERNEL_WORK_GROUP_SIZE
            ),
            device.get_info<size_t>(CL_DEVICE_LOCAL_MEM_SIZE),
            sort_by_key
        );

    const size_t global_size =
        work_group_size * static_cast<size_t>(
            std::ceil(float(count) / work_group_size)
        );

    kernel.set_arg(count_arg, static_cast<uint_>(count));
    kernel.set_arg(local_keys_arg, local_buffer<key_type>(work_group_size));
    if(sort_by_key) {
        kernel.set_arg(local_vals_arg, local_buffer<uchar_>(work_group_size));
    }

    queue.enqueue_1d_range_kernel(kernel, 0, global_size, work_group_size);
    // return size of the block
    return work_group_size;
}
InputIterator find_extrema_with_reduce(InputIterator first,
                                       InputIterator last,
                                       ::boost::compute::less<
                                           typename std::iterator_traits<
                                               InputIterator
                                           >::value_type
                                       >
                                       compare,
                                       const bool find_minimum,
                                       command_queue &queue)
{
    typedef typename std::iterator_traits<InputIterator>::difference_type difference_type;
    typedef typename std::iterator_traits<InputIterator>::value_type input_type;

    const context &context = queue.get_context();
    const device &device = queue.get_device();

    // Getting information about used queue and device
    const size_t compute_units_no = device.get_info<CL_DEVICE_MAX_COMPUTE_UNITS>();
    const size_t max_work_group_size = device.get_info<CL_DEVICE_MAX_WORK_GROUP_SIZE>();

    const size_t count = detail::iterator_range_size(first, last);

    std::string cache_key = std::string("__boost_find_extrema_with_reduce_")
        + type_name<input_type>();

    // load parameters
    boost::shared_ptr<parameter_cache> parameters =
        detail::parameter_cache::get_global_cache(device);

    // get preferred work group size and preferred number
    // of work groups per compute unit
    size_t work_group_size = parameters->get(cache_key, "wgsize", 256);
    size_t work_groups_per_cu = parameters->get(cache_key, "wgpcu", 64);

    // calculate work group size and number of work groups
    work_group_size = (std::min)(max_work_group_size, work_group_size);
    size_t work_groups_no = compute_units_no * work_groups_per_cu;
    work_groups_no = (std::min)(
        work_groups_no,
        static_cast<size_t>(std::ceil(float(count) / work_group_size))
    );

    // phase I: finding candidates for extremum

    // device buffors for extremum candidates and their indices
    // each work-group computes its candidate
    // zero-copy buffers are used to eliminate copying data back to host
    vector<input_type, ::boost::compute::pinned_allocator<input_type> >
        candidates(work_groups_no, context);
    vector<uint_, ::boost::compute::pinned_allocator <uint_> >
        candidates_idx(work_groups_no, context);

    // finding candidates for first extremum and their indices
    find_extrema_with_reduce(
        first, count, candidates.begin(), candidates_idx.begin(),
        work_groups_no, work_group_size, compare, find_minimum, queue
    );

    // phase II: finding extremum from among the candidates

    // mapping candidates and their indices to host
    input_type* candidates_host_ptr =
        static_cast<input_type*>(
            queue.enqueue_map_buffer(
                candidates.get_buffer(), command_queue::map_read,
                0, work_groups_no * sizeof(input_type)
            )
        );

    uint_* candidates_idx_host_ptr =
        static_cast<uint_*>(
            queue.enqueue_map_buffer(
                candidates_idx.get_buffer(), command_queue::map_read,
                0, work_groups_no * sizeof(uint_)
            )
        );

    input_type* i = candidates_host_ptr;
    uint_* idx = candidates_idx_host_ptr;
    uint_* extremum_idx = idx;
    input_type extremum = *candidates_host_ptr;
    i++; idx++;

    // find extremum (serial) from among the candidates on host
    if(!find_minimum) {
        while(idx != (candidates_idx_host_ptr + work_groups_no)) {
            input_type next = *i;
            bool compare_result =  next > extremum;
            bool equal = next == extremum;
            extremum = compare_result ? next : extremum;
            extremum_idx = compare_result ? idx : extremum_idx;
            extremum_idx = equal ? ((*extremum_idx < *idx) ? extremum_idx : idx) : extremum_idx;
            idx++, i++;
        }
    }
    else {
        while(idx != (candidates_idx_host_ptr + work_groups_no)) {
            input_type next = *i;
            bool compare_result = next < extremum;
            bool equal = next == extremum;
            extremum = compare_result ? next : extremum;
            extremum_idx = compare_result ? idx : extremum_idx;
            extremum_idx = equal ? ((*extremum_idx < *idx) ? extremum_idx : idx) : extremum_idx;
            idx++, i++;
        }
    }

    return first + static_cast<difference_type>(*extremum_idx);
}
Example #3
0
size_t reduce(InputIterator first,
              size_t count,
              OutputIterator result,
              size_t block_size,
              BinaryFunction function,
              command_queue &queue)
{
    typedef typename
        std::iterator_traits<InputIterator>::value_type
        input_type;
    typedef typename
        boost::compute::result_of<BinaryFunction(input_type, input_type)>::type
        result_type;

    const context &context = queue.get_context();
    size_t block_count = count / 2 / block_size;
    size_t total_block_count =
        static_cast<size_t>(std::ceil(float(count) / 2.f / float(block_size)));

    if(block_count != 0){
        meta_kernel k("block_reduce");
        size_t output_arg = k.add_arg<result_type *>(memory_object::global_memory, "output");
        size_t block_arg = k.add_arg<input_type *>(memory_object::local_memory, "block");

        k <<
            "const uint gid = get_global_id(0);\n" <<
            "const uint lid = get_local_id(0);\n" <<

            // copy values to local memory
            "block[lid] = " <<
                function(first[k.make_var<uint_>("gid*2+0")],
                         first[k.make_var<uint_>("gid*2+1")]) << ";\n" <<

            // perform reduction
            "for(uint i = 1; i < " << uint_(block_size) << "; i <<= 1){\n" <<
            "    barrier(CLK_LOCAL_MEM_FENCE);\n" <<
            "    uint mask = (i << 1) - 1;\n" <<
            "    if((lid & mask) == 0){\n" <<
            "        block[lid] = " <<
                         function(k.expr<input_type>("block[lid]"),
                                  k.expr<input_type>("block[lid+i]")) << ";\n" <<
            "    }\n" <<
            "}\n" <<

            // write block result to global output
            "if(lid == 0)\n" <<
            "    output[get_group_id(0)] = block[0];\n";

        kernel kernel = k.compile(context);
        kernel.set_arg(output_arg, result.get_buffer());
        kernel.set_arg(block_arg, local_buffer<input_type>(block_size));

        queue.enqueue_1d_range_kernel(kernel,
                                      0,
                                      block_count * block_size,
                                      block_size);
    }

    // serially reduce any leftovers
    if(block_count * block_size * 2 < count){
        size_t last_block_start = block_count * block_size * 2;

        meta_kernel k("extra_serial_reduce");
        size_t count_arg = k.add_arg<uint_>("count");
        size_t offset_arg = k.add_arg<uint_>("offset");
        size_t output_arg = k.add_arg<result_type *>(memory_object::global_memory, "output");
        size_t output_offset_arg = k.add_arg<uint_>("output_offset");

        k <<
            k.decl<result_type>("result") << " = \n" <<
                first[k.expr<uint_>("offset")] << ";\n" <<
            "for(uint i = offset + 1; i < count; i++)\n" <<
            "    result = " <<
                     function(k.var<result_type>("result"),
                              first[k.var<uint_>("i")]) << ";\n" <<
            "output[output_offset] = result;\n";

        kernel kernel = k.compile(context);
        kernel.set_arg(count_arg, static_cast<uint_>(count));
        kernel.set_arg(offset_arg, static_cast<uint_>(last_block_start));
        kernel.set_arg(output_arg, result.get_buffer());
        kernel.set_arg(output_offset_arg, static_cast<uint_>(block_count));

        queue.enqueue_task(kernel);
    }

    return total_block_count;
}
Example #4
0
/// Returns raw context id for the given queue.
inline context_id get_context_id(const command_queue &q) {
    return q.get_context().get();
}
Example #5
0
inline void inplace_reduce(Iterator first,
                           Iterator last,
                           BinaryFunction function,
                           command_queue &queue)
{
    typedef typename
        std::iterator_traits<Iterator>::value_type
        value_type;

    size_t input_size = iterator_range_size(first, last);
    if(input_size < 2){
        return;
    }

    const context &context = queue.get_context();

    size_t block_size = 64;
    size_t values_per_thread = 8;
    size_t block_count = input_size / (block_size * values_per_thread);
    if(block_count * block_size * values_per_thread != input_size)
        block_count++;

    vector<value_type> output(block_count, context);

    meta_kernel k("inplace_reduce");
    size_t input_arg = k.add_arg<value_type *>(memory_object::global_memory, "input");
    size_t input_size_arg = k.add_arg<const uint_>("input_size");
    size_t output_arg = k.add_arg<value_type *>(memory_object::global_memory, "output");
    size_t scratch_arg = k.add_arg<value_type *>(memory_object::local_memory, "scratch");
    k <<
        "const uint gid = get_global_id(0);\n" <<
        "const uint lid = get_local_id(0);\n" <<
        "const uint values_per_thread =\n"
            << uint_(values_per_thread) << ";\n" <<

        // thread reduce
        "const uint index = gid * values_per_thread;\n" <<
        "if(index < input_size){\n" <<
            k.decl<value_type>("sum") << " = input[index];\n" <<
            "for(uint i = 1;\n" <<
                 "i < values_per_thread && (index + i) < input_size;\n" <<
                 "i++){\n" <<
            "    sum = " <<
                     function(k.var<value_type>("sum"),
                              k.var<value_type>("input[index+i]")) << ";\n" <<
            "}\n" <<
            "scratch[lid] = sum;\n" <<
        "}\n" <<

        // local reduce
        "for(uint i = 1; i < get_local_size(0); i <<= 1){\n" <<
        "    barrier(CLK_LOCAL_MEM_FENCE);\n" <<
        "    uint mask = (i << 1) - 1;\n" <<
        "    uint next_index = (gid + i) * values_per_thread;\n"
        "    if((lid & mask) == 0 && next_index < input_size){\n" <<
        "        scratch[lid] = " <<
                     function(k.var<value_type>("scratch[lid]"),
                              k.var<value_type>("scratch[lid+i]")) << ";\n" <<
        "    }\n" <<
        "}\n" <<

        // write output for block
        "if(lid == 0){\n" <<
        "    output[get_group_id(0)] = scratch[0];\n" <<
        "}\n"
        ;

    const buffer *input_buffer = &first.get_buffer();
    const buffer *output_buffer = &output.get_buffer();

    kernel kernel = k.compile(context);

    while(input_size > 1){
        kernel.set_arg(input_arg, *input_buffer);
        kernel.set_arg(input_size_arg, static_cast<uint_>(input_size));
        kernel.set_arg(output_arg, *output_buffer);
        kernel.set_arg(scratch_arg, local_buffer<value_type>(block_size));

        queue.enqueue_1d_range_kernel(kernel,
                                      0,
                                      block_count * block_size,
                                      block_size);

        input_size =
            static_cast<size_t>(
                std::ceil(float(input_size) / (block_size * values_per_thread)
            )
        );

        block_count = input_size / (block_size * values_per_thread);
        if(block_count * block_size * values_per_thread != input_size)
            block_count++;

        std::swap(input_buffer, output_buffer);
    }

    if(input_buffer != &first.get_buffer()){
        ::boost::compute::copy(output.begin(),
                               output.begin() + 1,
                               first,
                               queue);
    }
}
Example #6
0
/// Create command queue on the same context and device as the given one.
inline command_queue duplicate_queue(const command_queue &q) {
    return command_queue(q.get_context(), q.get_device(), q.get_properties());
}
Example #7
0
/// Returns device associated with the given queue.
inline device get_device(const command_queue &q) {
    return q.get_device();
}
Example #8
0
inline OutputIterator scan_on_cpu(InputIterator first,
                                  InputIterator last,
                                  OutputIterator result,
                                  bool exclusive,
                                  T init,
                                  BinaryOperator op,
                                  command_queue &queue)
{
    if(first == last){
        return result;
    }

    typedef typename
        std::iterator_traits<InputIterator>::value_type input_type;
    typedef typename
        std::iterator_traits<OutputIterator>::value_type output_type;

    const context &context = queue.get_context();

    // create scan kernel
    meta_kernel k("scan_on_cpu");

    // Arguments
    size_t n_arg = k.add_arg<ulong_>("n");
    size_t init_arg = k.add_arg<output_type>("initial_value");

    if(!exclusive){
        k <<
            k.decl<const ulong_>("start_idx") << " = 1;\n" <<
            k.decl<output_type>("sum") << " = " << first[0] << ";\n" <<
            result[0] << " = sum;\n";
    }
    else {
        k <<
            k.decl<const ulong_>("start_idx") << " = 0;\n" <<
            k.decl<output_type>("sum") << " = initial_value;\n";
    }

    k <<
        "for(ulong i = start_idx; i < n; i++){\n" <<
        k.decl<const input_type>("x") << " = "
            << first[k.var<ulong_>("i")] << ";\n";

    if(exclusive){
        k << result[k.var<ulong_>("i")] << " = sum;\n";
    }

    k << "    sum = "
        << op(k.var<output_type>("sum"), k.var<output_type>("x"))
        << ";\n";

    if(!exclusive){
        k << result[k.var<ulong_>("i")] << " = sum;\n";
    }

    k << "}\n";

    // compile scan kernel
    kernel scan_kernel = k.compile(context);

    // setup kernel arguments
    size_t n = detail::iterator_range_size(first, last);
    scan_kernel.set_arg<ulong_>(n_arg, n);
    scan_kernel.set_arg<output_type>(init_arg, static_cast<output_type>(init));

    // execute the kernel
    queue.enqueue_1d_range_kernel(scan_kernel, 0, 1, 1);

    // return iterator pointing to the end of the result range
    return result + n;
}
Example #9
0
inline void radix_sort_impl(const buffer_iterator<T> first,
                            const buffer_iterator<T> last,
                            const buffer_iterator<T2> values_first,
                            const bool ascending,
                            command_queue &queue)
{

    typedef T value_type;
    typedef typename radix_sort_value_type<sizeof(T)>::type sort_type;

    const device &device = queue.get_device();
    const context &context = queue.get_context();


    // if we have a valid values iterator then we are doing a
    // sort by key and have to set up the values buffer
    bool sort_by_key = (values_first.get_buffer().get() != 0);

    // load (or create) radix sort program
    std::string cache_key =
        std::string("__boost_radix_sort_") + type_name<value_type>();

    if(sort_by_key){
        cache_key += std::string("_with_") + type_name<T2>();
    }

    boost::shared_ptr<program_cache> cache =
        program_cache::get_global_cache(context);
    boost::shared_ptr<parameter_cache> parameters =
        detail::parameter_cache::get_global_cache(device);

    // sort parameters
    const uint_ k = parameters->get(cache_key, "k", 4);
    const uint_ k2 = 1 << k;
    const uint_ block_size = parameters->get(cache_key, "tpb", 128);

    // sort program compiler options
    std::stringstream options;
    options << "-DK_BITS=" << k;
    options << " -DT=" << type_name<sort_type>();
    options << " -DBLOCK_SIZE=" << block_size;

    if(boost::is_floating_point<value_type>::value){
        options << " -DIS_FLOATING_POINT";
    }

    if(boost::is_signed<value_type>::value){
        options << " -DIS_SIGNED";
    }

    if(sort_by_key){
        options << " -DSORT_BY_KEY";
        options << " -DT2=" << type_name<T2>();
        options << enable_double<T2>();
    }

    if(ascending){
        options << " -DASC";
    }

    // load radix sort program
    program radix_sort_program = cache->get_or_build(
        cache_key, options.str(), radix_sort_source, context
    );

    kernel count_kernel(radix_sort_program, "count");
    kernel scan_kernel(radix_sort_program, "scan");
    kernel scatter_kernel(radix_sort_program, "scatter");

    size_t count = detail::iterator_range_size(first, last);

    uint_ block_count = static_cast<uint_>(count / block_size);
    if(block_count * block_size != count){
        block_count++;
    }

    // setup temporary buffers
    vector<value_type> output(count, context);
    vector<T2> values_output(sort_by_key ? count : 0, context);
    vector<uint_> offsets(k2, context);
    vector<uint_> counts(block_count * k2, context);

    const buffer *input_buffer = &first.get_buffer();
    uint_ input_offset = static_cast<uint_>(first.get_index());
    const buffer *output_buffer = &output.get_buffer();
    uint_ output_offset = 0;
    const buffer *values_input_buffer = &values_first.get_buffer();
    uint_ values_input_offset = static_cast<uint_>(values_first.get_index());
    const buffer *values_output_buffer = &values_output.get_buffer();
    uint_ values_output_offset = 0;

    for(uint_ i = 0; i < sizeof(sort_type) * CHAR_BIT / k; i++){
        // write counts
        count_kernel.set_arg(0, *input_buffer);
        count_kernel.set_arg(1, input_offset);
        count_kernel.set_arg(2, static_cast<uint_>(count));
        count_kernel.set_arg(3, counts);
        count_kernel.set_arg(4, offsets);
        count_kernel.set_arg(5, block_size * sizeof(uint_), 0);
        count_kernel.set_arg(6, i * k);
        queue.enqueue_1d_range_kernel(count_kernel,
                                      0,
                                      block_count * block_size,
                                      block_size);

        // scan counts
        if(k == 1){
            typedef uint2_ counter_type;
            ::boost::compute::exclusive_scan(
                make_buffer_iterator<counter_type>(counts.get_buffer(), 0),
                make_buffer_iterator<counter_type>(counts.get_buffer(), counts.size() / 2),
                make_buffer_iterator<counter_type>(counts.get_buffer()),
                queue
            );
        }
        else if(k == 2){
            typedef uint4_ counter_type;
            ::boost::compute::exclusive_scan(
                make_buffer_iterator<counter_type>(counts.get_buffer(), 0),
                make_buffer_iterator<counter_type>(counts.get_buffer(), counts.size() / 4),
                make_buffer_iterator<counter_type>(counts.get_buffer()),
                queue
            );
        }
        else if(k == 4){
            typedef uint16_ counter_type;
            ::boost::compute::exclusive_scan(
                make_buffer_iterator<counter_type>(counts.get_buffer(), 0),
                make_buffer_iterator<counter_type>(counts.get_buffer(), counts.size() / 16),
                make_buffer_iterator<counter_type>(counts.get_buffer()),
                queue
            );
        }
        else {
            BOOST_ASSERT(false && "unknown k");
            break;
        }

        // scan global offsets
        scan_kernel.set_arg(0, counts);
        scan_kernel.set_arg(1, offsets);
        scan_kernel.set_arg(2, block_count);
        queue.enqueue_task(scan_kernel);

        // scatter values
        scatter_kernel.set_arg(0, *input_buffer);
        scatter_kernel.set_arg(1, input_offset);
        scatter_kernel.set_arg(2, static_cast<uint_>(count));
        scatter_kernel.set_arg(3, i * k);
        scatter_kernel.set_arg(4, counts);
        scatter_kernel.set_arg(5, offsets);
        scatter_kernel.set_arg(6, *output_buffer);
        scatter_kernel.set_arg(7, output_offset);
        if(sort_by_key){
            scatter_kernel.set_arg(8, *values_input_buffer);
            scatter_kernel.set_arg(9, values_input_offset);
            scatter_kernel.set_arg(10, *values_output_buffer);
            scatter_kernel.set_arg(11, values_output_offset);
        }
        queue.enqueue_1d_range_kernel(scatter_kernel,
                                      0,
                                      block_count * block_size,
                                      block_size);

        // swap buffers
        std::swap(input_buffer, output_buffer);
        std::swap(values_input_buffer, values_output_buffer);
        std::swap(input_offset, output_offset);
        std::swap(values_input_offset, values_output_offset);
    }
}
Example #10
0
 void start()
 {
     _actorStarted = true; 
     _actorQueue.start(); 
     _actorThread = std::thread( &actor<COMMAND,RETURN_VALUE>::_entry_point, this );
 }
Example #11
0
 void stop()
 {
     _actorStarted = false; 
     _actorQueue.stop(); 
     _actorThread.join();
 }
Example #12
0
inline OutputIterator scan_on_cpu(InputIterator first,
                                  InputIterator last,
                                  OutputIterator result,
                                  bool exclusive,
                                  T init,
                                  BinaryOperator op,
                                  command_queue &queue)
{
    typedef typename
        std::iterator_traits<InputIterator>::value_type input_type;
    typedef typename
        std::iterator_traits<OutputIterator>::value_type output_type;

    const context &context = queue.get_context();
    const device &device = queue.get_device();
    const size_t compute_units = queue.get_device().compute_units();

    boost::shared_ptr<parameter_cache> parameters =
        detail::parameter_cache::get_global_cache(device);

    std::string cache_key =
        "__boost_scan_cpu_" + boost::lexical_cast<std::string>(sizeof(T));

    // for inputs smaller than serial_scan_threshold
    // serial_scan algorithm is used
    uint_ serial_scan_threshold =
        parameters->get(cache_key, "serial_scan_threshold", 16384 * sizeof(T));
    serial_scan_threshold =
        (std::max)(serial_scan_threshold, uint_(compute_units));

    size_t count = detail::iterator_range_size(first, last);
    if(count == 0){
        return result;
    }
    else if(count < serial_scan_threshold) {
        return serial_scan(first, last, result, exclusive, init, op, queue);
    }

    buffer block_partial_sums(context, sizeof(output_type) * compute_units );

    // create scan kernel
    meta_kernel k("scan_on_cpu_block_scan");

    // Arguments
    size_t count_arg = k.add_arg<uint_>("count");
    size_t init_arg = k.add_arg<output_type>("initial_value");
    size_t block_partial_sums_arg =
        k.add_arg<output_type *>(memory_object::global_memory, "block_partial_sums");

    k <<
        "uint block = " <<
            "(uint)ceil(((float)count)/(get_global_size(0) + 1));\n" <<
        "uint index = get_global_id(0) * block;\n" <<
        "uint end = min(count, index + block);\n";

    if(!exclusive){
        k <<
            k.decl<output_type>("sum") << " = " <<
                first[k.var<uint_>("index")] << ";\n" <<
            result[k.var<uint_>("index")] << " = sum;\n" <<
            "index++;\n";
    }
    else {
        k <<
            k.decl<output_type>("sum") << ";\n" <<
            "if(index == 0){\n" <<
                "sum = initial_value;\n" <<
            "}\n" <<
            "else {\n" <<
                "sum = " << first[k.var<uint_>("index")] << ";\n" <<
                "index++;\n" <<
            "}\n";
    }

    k <<
        "while(index < end){\n" <<
            // load next value
            k.decl<const input_type>("value") << " = "
                << first[k.var<uint_>("index")] << ";\n";

    if(exclusive){
        k <<
            "if(get_global_id(0) == 0){\n" <<
                result[k.var<uint_>("index")] << " = sum;\n" <<
            "}\n";
    }
    k <<
            "sum = " << op(k.var<output_type>("sum"),
                           k.var<output_type>("value")) << ";\n";

    if(!exclusive){
        k <<
            "if(get_global_id(0) == 0){\n" <<
                result[k.var<uint_>("index")] << " = sum;\n" <<
            "}\n";
    }

    k <<
            "index++;\n" <<
        "}\n" << // end while
        "block_partial_sums[get_global_id(0)] = sum;\n";

    // compile scan kernel
    kernel block_scan_kernel = k.compile(context);

    // setup kernel arguments
    block_scan_kernel.set_arg(count_arg, static_cast<uint_>(count));
    block_scan_kernel.set_arg(init_arg, static_cast<output_type>(init));
    block_scan_kernel.set_arg(block_partial_sums_arg, block_partial_sums);

    // execute the kernel
    size_t global_work_size = compute_units;
    queue.enqueue_1d_range_kernel(block_scan_kernel, 0, global_work_size, 0);

    // scan is done
    if(compute_units < 2) {
        return result + count;
    }

    // final scan kernel
    meta_kernel l("scan_on_cpu_final_scan");

    // Arguments
    count_arg = l.add_arg<uint_>("count");
    block_partial_sums_arg =
        l.add_arg<output_type *>(memory_object::global_memory, "block_partial_sums");

    l <<
        "uint block = " <<
            "(uint)ceil(((float)count)/(get_global_size(0) + 1));\n" <<
        "uint index = block + get_global_id(0) * block;\n" <<
        "uint end = min(count, index + block);\n" <<

        k.decl<output_type>("sum") << " = block_partial_sums[0];\n" <<
        "for(uint i = 0; i < get_global_id(0); i++) {\n" <<
            "sum = " << op(k.var<output_type>("sum"),
                           k.var<output_type>("block_partial_sums[i + 1]")) << ";\n" <<
        "}\n" <<

        "while(index < end){\n";
    if(exclusive){
        l <<
            l.decl<output_type>("value") << " = "
                << first[k.var<uint_>("index")] << ";\n" <<
            result[k.var<uint_>("index")] << " = sum;\n" <<
            "sum = " << op(k.var<output_type>("sum"),
                           k.var<output_type>("value")) << ";\n";
    }
    else {
        l <<
            "sum = " << op(k.var<output_type>("sum"),
                           first[k.var<uint_>("index")]) << ";\n" <<
            result[k.var<uint_>("index")] << " = sum;\n";
    }
    l <<
            "index++;\n" <<
        "}\n";


    // compile scan kernel
    kernel final_scan_kernel = l.compile(context);

    // setup kernel arguments
    final_scan_kernel.set_arg(count_arg, static_cast<uint_>(count));
    final_scan_kernel.set_arg(block_partial_sums_arg, block_partial_sums);

    // execute the kernel
    global_work_size = compute_units;
    queue.enqueue_1d_range_kernel(final_scan_kernel, 0, global_work_size, 0);

    // return iterator pointing to the end of the result range
    return result + count;
}
Example #13
0
/// Create command queue on the same context and device as the given one.
inline command_queue duplicate_queue(const command_queue &q) {
    return command_queue(q.context(), q.device(), q.flags());
}
Example #14
0
/// Binds the specified CUDA context to the calling CPU thread.
inline void select_context(const command_queue &q) {
    q.context().set_current();
}
static inline std::vector<uint>
pad_vector(command_queue &q, const V &v, uint x) {
   std::vector<uint> w { v.begin(), v.end() };
   w.resize(q.device().max_block_size().size(), x);
   return w;
}
Example #16
0
/// Create and build a program from source string.
inline vex::backend::program build_sources(
        const 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

    std::string compile_options = options + " " + get_compile_options(queue);

    queue.context().set_current();

    auto cc = queue.device().compute_capability();
    std::ostringstream ccstr;
    ccstr << std::get<0>(cc) << std::get<1>(cc);

    sha1_hasher sha1;
    sha1.process(source)
        .process(queue.device().name())
        .process(compile_options)
        .process(ccstr.str())
        ;

    std::string hash = static_cast<std::string>(sha1);

    // Write source to a .cu file
    std::string basename = program_binaries_path(hash, true) + "kernel";
    std::string ptxfile  = basename + ".ptx";

    if ( !boost::filesystem::exists(ptxfile) ) {
        std::string cufile = basename + ".cu";

        {
            std::ofstream f(cufile);
            f << source;
        }

        // Compile the source to ptx.
        std::ostringstream cmdline;
        cmdline
            << "nvcc -ptx -O3"
            << " -arch=sm_" << std::get<0>(cc) << std::get<1>(cc)
            << " " << compile_options
            << " -o " << ptxfile << " " << cufile;
        if (0 != system(cmdline.str().c_str()) ) {
#ifndef VEXCL_SHOW_KERNELS
            std::cerr << source << std::endl;
#endif

            vex::detail::print_backtrace();
            throw std::runtime_error("nvcc invocation failed");
        }
    }

    // Load the compiled ptx.
    CUmodule prg;
    cuda_check( cuModuleLoad(&prg, ptxfile.c_str()) );

    return program(queue.context(), prg);
}
Example #17
0
 bool operator()(const command_queue &a, const command_queue &b) const {
     return a.get() < b.get();
 }
Example #18
0
 /// Standard number of workgroups to launch on a device.
 static inline size_t num_workgroups(const command_queue &q) {
     return 8 * q.device().multiprocessor_count();
 }
Example #19
0
/// Checks if the compute device is CPU.
inline bool is_cpu(const command_queue &q) {
    return q.get_device().get_info<cl_device_type>(CL_DEVICE_TYPE) & CL_DEVICE_TYPE_CPU;
}
Example #20
0
 /// The size in bytes of shared memory per block available for this kernel.
 size_t max_shared_memory_per_block(const command_queue &q) const {
     return q.device().max_shared_memory_per_block() -
         shared_size_bytes();
 }
Example #21
0
/// Returns id of the device associated with the given queue.
inline device_id get_device_id(const command_queue &q) {
    return q.get_device().get();
}
inline size_t serial_reduce_by_key(InputKeyIterator keys_first,
                                   InputKeyIterator keys_last,
                                   InputValueIterator values_first,
                                   OutputKeyIterator keys_result,
                                   OutputValueIterator values_result,
                                   BinaryFunction function,
                                   BinaryPredicate predicate,
                                   command_queue &queue)
{
    typedef typename
        std::iterator_traits<InputValueIterator>::value_type value_type;
    typedef typename
        std::iterator_traits<InputKeyIterator>::value_type key_type;
    typedef typename
        ::boost::compute::result_of<BinaryFunction(value_type, value_type)>::type result_type;

    const context &context = queue.get_context();
    size_t count = detail::iterator_range_size(keys_first, keys_last);
    if(count < 1){
        return count;
    }

    meta_kernel k("serial_reduce_by_key");
    size_t count_arg = k.add_arg<uint_>("count");
    size_t result_size_arg = k.add_arg<uint_ *>(memory_object::global_memory,
                                                "result_size");

    k <<
        k.decl<result_type>("result") <<
            " = " << values_first[0] << ";\n" <<
        k.decl<key_type>("previous_key") << " = " << keys_first[0] << ";\n" <<
        k.decl<result_type>("value") << ";\n" <<
        k.decl<key_type>("key") << ";\n" <<

        k.decl<uint_>("size") << " = 1;\n" <<

        keys_result[0] << " = previous_key;\n" <<
        values_result[0] << " = result;\n" <<

        "for(ulong i = 1; i < count; i++) {\n" <<
        "    value = " << values_first[k.var<uint_>("i")] << ";\n" <<
        "    key = " << keys_first[k.var<uint_>("i")] << ";\n" <<
        "    if (" << predicate(k.var<key_type>("previous_key"),
                                k.var<key_type>("key")) << ") {\n" <<

        "        result = " << function(k.var<result_type>("result"),
                                        k.var<result_type>("value")) << ";\n" <<
        "    }\n " <<
        "    else { \n" <<
                 keys_result[k.var<uint_>("size - 1")] << " = previous_key;\n" <<
                 values_result[k.var<uint_>("size - 1")] << " = result;\n" <<
        "        result = value;\n" <<
        "        size++;\n" <<
        "    } \n" <<
        "    previous_key = key;\n" <<
        "}\n" <<
        keys_result[k.var<uint_>("size - 1")] << " = previous_key;\n" <<
        values_result[k.var<uint_>("size - 1")] << " = result;\n" <<
        "*result_size = size;";

    kernel kernel = k.compile(context);

    scalar<uint_> result_size(context);
    kernel.set_arg(result_size_arg, result_size.get_buffer());
    kernel.set_arg(count_arg, static_cast<uint_>(count));

    queue.enqueue_task(kernel);

    return static_cast<size_t>(result_size.read(queue));
}
Example #23
0
/// Returns context for the given queue.
inline context get_context(const command_queue &q) {
    return q.get_context();
}
Example #24
0
inline void reduce_on_gpu(InputIterator first,
                          InputIterator last,
                          buffer_iterator<T> result,
                          Function function,
                          command_queue &queue)
{
    const device &device = queue.get_device();
    const context &context = queue.get_context();

    detail::meta_kernel k("reduce");
    k.add_arg<const T*>(memory_object::global_memory, "input");
    k.add_arg<const uint_>("offset");
    k.add_arg<const uint_>("count");
    k.add_arg<T*>(memory_object::global_memory, "output");
    k.add_arg<const uint_>("output_offset");

    k <<
        k.decl<const uint_>("block_offset") << " = get_group_id(0) * VPT * TPB;\n" <<
        "__global const " << type_name<T>() << " *block = input + offset + block_offset;\n" <<
        k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<

        "__local " << type_name<T>() << " scratch[TPB];\n" <<
        // private reduction
        k.decl<T>("sum") << " = 0;\n" <<
        "for(uint i = 0; i < VPT; i++){\n" <<
        "    if(block_offset + lid + i*TPB < count){\n" <<
        "        sum = sum + block[lid+i*TPB]; \n" <<
        "    }\n" <<
        "}\n" <<

        "scratch[lid] = sum;\n";

    // discrimination on vendor name
    if(is_nvidia_device(device))
        k << ReduceBody<T,true>::body();
    else
        k << ReduceBody<T,false>::body();

    k <<
        // write sum to output
         "if(lid == 0){\n" <<
         "    output[output_offset + get_group_id(0)] = scratch[0];\n" <<
         "}\n";

    std::string cache_key = std::string("__boost_reduce_on_gpu_") + type_name<T>();

    // load parameters
    boost::shared_ptr<parameter_cache> parameters =
        detail::parameter_cache::get_global_cache(device);

    uint_ vpt = parameters->get(cache_key, "vpt", 8);
    uint_ tpb = parameters->get(cache_key, "tpb", 128);

    // reduce program compiler flags
    std::stringstream options;
    options << "-DT=" << type_name<T>()
            << " -DVPT=" << vpt
            << " -DTPB=" << tpb;

    // load program
    boost::shared_ptr<program_cache> cache =
        program_cache::get_global_cache(context);

    program reduce_program = cache->get_or_build(
        cache_key, options.str(), k.source(), context
    );

    // create reduce kernel
    kernel reduce_kernel(reduce_program, "reduce");

    size_t count = std::distance(first, last);

    // first pass, reduce from input to ping
    buffer ping(context, std::ceil(float(count) / vpt / tpb) * sizeof(T));
    initial_reduce(first, last, ping, function, reduce_kernel, vpt, tpb, queue);

    // update count after initial reduce
    count = std::ceil(float(count) / vpt / tpb);

    // middle pass(es), reduce between ping and pong
    const buffer *input_buffer = &ping;
    buffer pong(context, count / vpt / tpb * sizeof(T));
    const buffer *output_buffer = &pong;
    if(count > vpt * tpb){
        while(count > vpt * tpb){
            reduce_kernel.set_arg(0, *input_buffer);
            reduce_kernel.set_arg(1, uint_(0));
            reduce_kernel.set_arg(2, uint_(count));
            reduce_kernel.set_arg(3, *output_buffer);
            reduce_kernel.set_arg(4, uint_(0));

            size_t work_size = std::ceil(float(count) / vpt);
            if(work_size % tpb != 0){
                work_size += tpb - work_size % tpb;
            }
            queue.enqueue_1d_range_kernel(reduce_kernel, 0, work_size, tpb);

            std::swap(input_buffer, output_buffer);
            count = std::ceil(float(count) / vpt / tpb);
        }
    }

    // final pass, reduce from ping/pong to result
    reduce_kernel.set_arg(0, *input_buffer);
    reduce_kernel.set_arg(1, uint_(0));
    reduce_kernel.set_arg(2, uint_(count));
    reduce_kernel.set_arg(3, result.get_buffer());
    reduce_kernel.set_arg(4, uint_(result.get_index()));

    queue.enqueue_1d_range_kernel(reduce_kernel, 0, tpb, tpb);
}
Example #25
0
InputIterator find_extrema_with_reduce(InputIterator first,
                                       InputIterator last,
                                       Compare compare,
                                       const bool find_minimum,
                                       command_queue &queue)
{
    typedef typename std::iterator_traits<InputIterator>::difference_type difference_type;
    typedef typename std::iterator_traits<InputIterator>::value_type input_type;

    const context &context = queue.get_context();
    const device &device = queue.get_device();

    // Getting information about used queue and device
    const size_t compute_units_no = device.get_info<CL_DEVICE_MAX_COMPUTE_UNITS>();
    const size_t max_work_group_size = device.get_info<CL_DEVICE_MAX_WORK_GROUP_SIZE>();

    const size_t count = detail::iterator_range_size(first, last);

    std::string cache_key = std::string("__boost_find_extrema_with_reduce_")
        + type_name<input_type>();

    // load parameters
    boost::shared_ptr<parameter_cache> parameters =
        detail::parameter_cache::get_global_cache(device);

    // get preferred work group size and preferred number
    // of work groups per compute unit
    size_t work_group_size = parameters->get(cache_key, "wgsize", 256);
    size_t work_groups_per_cu = parameters->get(cache_key, "wgpcu", 100);

    // calculate work group size and number of work groups
    work_group_size = (std::min)(max_work_group_size, work_group_size);
    size_t work_groups_no = compute_units_no * work_groups_per_cu;
    work_groups_no = (std::min)(
        work_groups_no,
        static_cast<size_t>(std::ceil(float(count) / work_group_size))
    );

    // phase I: finding candidates for extremum

    // device buffors for extremum candidates and their indices
    // each work-group computes its candidate
    vector<input_type> candidates(work_groups_no, context);
    vector<uint_> candidates_idx(work_groups_no, context);

    // finding candidates for first extremum and their indices
    find_extrema_with_reduce(
        first, count, candidates.begin(), candidates_idx.begin(),
        work_groups_no, work_group_size, compare, find_minimum, queue
    );

    // phase II: finding extremum from among the candidates

    // zero-copy buffers for final result (value and index)
    vector<input_type, ::boost::compute::pinned_allocator<input_type> >
        result(1, context);
    vector<uint_, ::boost::compute::pinned_allocator<uint_> >
        result_idx(1, context);

    // get extremum from among the candidates
    find_extrema_with_reduce(
        candidates.begin(), candidates_idx.begin(), work_groups_no, result.begin(),
        result_idx.begin(), 1, work_group_size, compare, find_minimum, true, queue
    );

    // mapping extremum index to host
    uint_* result_idx_host_ptr =
        static_cast<uint_*>(
            queue.enqueue_map_buffer(
                result_idx.get_buffer(), command_queue::map_read,
                0, sizeof(uint_)
            )
        );

    return first + static_cast<difference_type>(*result_idx_host_ptr);
}
inline InputIterator find_extrema_on_cpu(InputIterator first,
                                         InputIterator last,
                                         Compare compare,
                                         const bool find_minimum,
                                         command_queue &queue)
{
    typedef typename std::iterator_traits<InputIterator>::value_type input_type;
    typedef typename std::iterator_traits<InputIterator>::difference_type difference_type;
    size_t count = iterator_range_size(first, last);

    const device &device = queue.get_device();
    const uint_ compute_units = queue.get_device().compute_units();

    boost::shared_ptr<parameter_cache> parameters =
        detail::parameter_cache::get_global_cache(device);
    std::string cache_key =
        "__boost_find_extrema_cpu_"
            + boost::lexical_cast<std::string>(sizeof(input_type));

    // for inputs smaller than serial_find_extrema_threshold
    // serial_find_extrema algorithm is used
    uint_ serial_find_extrema_threshold = parameters->get(
        cache_key,
        "serial_find_extrema_threshold",
        16384 * sizeof(input_type)
    );
    serial_find_extrema_threshold =
        (std::max)(serial_find_extrema_threshold, uint_(2 * compute_units));

    const context &context = queue.get_context();
    if(count < serial_find_extrema_threshold) {
        return serial_find_extrema(first, last, compare, find_minimum, queue);
    }

    meta_kernel k("find_extrema_on_cpu");
    buffer output(context, sizeof(input_type) * compute_units);
    buffer output_idx(
        context, sizeof(uint_) * compute_units,
        buffer::read_write | buffer::alloc_host_ptr
    );

    size_t count_arg = k.add_arg<uint_>("count");
    size_t output_arg =
        k.add_arg<input_type *>(memory_object::global_memory, "output");
    size_t output_idx_arg =
        k.add_arg<uint_ *>(memory_object::global_memory, "output_idx");

    k <<
        "uint block = " <<
            "(uint)ceil(((float)count)/get_global_size(0));\n" <<
        "uint index = get_global_id(0) * block;\n" <<
        "uint end = min(count, index + block);\n" <<

        "uint value_index = index;\n" <<
        k.decl<input_type>("value") << " = " << first[k.var<uint_>("index")] << ";\n" <<

        "index++;\n" <<
        "while(index < end){\n" <<
            k.decl<input_type>("candidate") <<
                " = " << first[k.var<uint_>("index")] << ";\n" <<
        "#ifndef BOOST_COMPUTE_FIND_MAXIMUM\n" <<
            "bool compare = " << compare(k.var<input_type>("candidate"),
                                         k.var<input_type>("value")) << ";\n" <<
        "#else\n" <<
            "bool compare = " << compare(k.var<input_type>("value"),
                                         k.var<input_type>("candidate")) << ";\n" <<
        "#endif\n" <<
            "value = compare ? candidate : value;\n" <<
            "value_index = compare ? index : value_index;\n" <<
            "index++;\n" <<
        "}\n" <<
        "output[get_global_id(0)] = value;\n" <<
        "output_idx[get_global_id(0)] = value_index;\n";

    size_t global_work_size = compute_units;
    std::string options;
    if(!find_minimum){
        options = "-DBOOST_COMPUTE_FIND_MAXIMUM";
    }
    kernel kernel = k.compile(context, options);

    kernel.set_arg(count_arg, static_cast<uint_>(count));
    kernel.set_arg(output_arg, output);
    kernel.set_arg(output_idx_arg, output_idx);
    queue.enqueue_1d_range_kernel(kernel, 0, global_work_size, 0);

    buffer_iterator<input_type> result = serial_find_extrema(
        make_buffer_iterator<input_type>(output),
        make_buffer_iterator<input_type>(output, global_work_size),
        compare,
        find_minimum,
        queue
    );

    uint_* output_idx_host_ptr =
        static_cast<uint_*>(
            queue.enqueue_map_buffer(
                output_idx, command_queue::map_read,
                0, global_work_size * sizeof(uint_)
            )
        );

    difference_type extremum_idx =
        static_cast<difference_type>(*(output_idx_host_ptr + result.get_index()));
    return first + extremum_idx;
}
Example #27
0
inline void find_extrema_with_reduce(InputIterator input,
                                     vector<uint_>::iterator input_idx,
                                     size_t count,
                                     ResultIterator result,
                                     vector<uint_>::iterator result_idx,
                                     size_t work_groups_no,
                                     size_t work_group_size,
                                     Compare compare,
                                     const bool find_minimum,
                                     const bool use_input_idx,
                                     command_queue &queue)
{
    typedef typename std::iterator_traits<InputIterator>::value_type input_type;

    const context &context = queue.get_context();

    meta_kernel k("find_extrema_reduce");
    size_t count_arg = k.add_arg<uint_>("count");
    size_t block_arg = k.add_arg<input_type *>(memory_object::local_memory, "block");
    size_t block_idx_arg = k.add_arg<uint_ *>(memory_object::local_memory, "block_idx");

    k <<
        // Work item global id
        k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<

        // Index of element that will be read from input buffer
        k.decl<uint_>("idx") << " = gid;\n" <<

        k.decl<input_type>("acc") << ";\n" <<
        k.decl<uint_>("acc_idx") << ";\n" <<
        "if(gid < count) {\n" <<
            // Real index of currently best element
            "#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
            k.var<uint_>("acc_idx") << " = " << input_idx[k.var<uint_>("idx")] << ";\n" <<
            "#else\n" <<
            k.var<uint_>("acc_idx") << " = idx;\n" <<
            "#endif\n" <<

            // Init accumulator with first[get_global_id(0)]
            "acc = " << input[k.var<uint_>("idx")] << ";\n" <<
            "idx += get_global_size(0);\n" <<
        "}\n" <<

        k.decl<bool>("compare_result") << ";\n" <<
        k.decl<bool>("equal") << ";\n\n" <<
        "while( idx < count ){\n" <<
            // Next element
            k.decl<input_type>("next") << " = " << input[k.var<uint_>("idx")] << ";\n" <<
            "#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
            k.decl<input_type>("next_idx") << " = " << input_idx[k.var<uint_>("idx")] << ";\n" <<
            "#endif\n" <<

            // Comparison between currently best element (acc) and next element
            "#ifdef BOOST_COMPUTE_FIND_MAXIMUM\n" <<
            "compare_result = " << compare(k.var<input_type>("next"),
                                           k.var<input_type>("acc")) << ";\n" <<
            "# ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
            "equal = !compare_result && !" <<
                compare(k.var<input_type>("acc"),
                        k.var<input_type>("next")) << ";\n" <<
            "# endif\n" <<
            "#else\n" <<
            "compare_result = " << compare(k.var<input_type>("acc"),
                                           k.var<input_type>("next")) << ";\n" <<
            "# ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
            "equal = !compare_result && !" <<
                compare(k.var<input_type>("next"),
                        k.var<input_type>("acc")) << ";\n" <<
            "# endif\n" <<
            "#endif\n" <<

            // save the winner
            "acc = compare_result ? acc : next;\n" <<
            "#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
            "acc_idx = compare_result ? " <<
                "acc_idx : " <<
                "(equal ? min(acc_idx, next_idx) : next_idx);\n" <<
            "#else\n" <<
            "acc_idx = compare_result ? acc_idx : idx;\n" <<
            "#endif\n" <<
            "idx += get_global_size(0);\n" <<
        "}\n\n" <<

        // Work item local id
        k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
        "block[lid] = acc;\n" <<
        "block_idx[lid] = acc_idx;\n" <<
        "barrier(CLK_LOCAL_MEM_FENCE);\n" <<

        k.decl<uint_>("group_offset") <<
            " = count - (get_local_size(0) * get_group_id(0));\n\n";

    k <<
        "#pragma unroll\n"
        "for(" << k.decl<uint_>("offset") << " = " << uint_(work_group_size) << " / 2; offset > 0; " <<
             "offset = offset / 2) {\n" <<
             "if((lid < offset) && ((lid + offset) < group_offset)) { \n" <<
                 k.decl<input_type>("mine") << " = block[lid];\n" <<
                 k.decl<input_type>("other") << " = block[lid+offset];\n" <<
                 "#ifdef BOOST_COMPUTE_FIND_MAXIMUM\n" <<
                 "compare_result = " << compare(k.var<input_type>("other"),
                                                k.var<input_type>("mine")) << ";\n" <<
                 "equal = !compare_result && !" <<
                     compare(k.var<input_type>("mine"),
                             k.var<input_type>("other")) << ";\n" <<
                 "#else\n" <<
                 "compare_result = " << compare(k.var<input_type>("mine"),
                                                k.var<input_type>("other")) << ";\n" <<
                 "equal = !compare_result && !" <<
                     compare(k.var<input_type>("other"),
                             k.var<input_type>("mine")) << ";\n" <<
                 "#endif\n" <<
                 "block[lid] = compare_result ? mine : other;\n" <<
                 k.decl<uint_>("mine_idx") << " = block_idx[lid];\n" <<
                 k.decl<uint_>("other_idx") << " = block_idx[lid+offset];\n" <<
                 "block_idx[lid] = compare_result ? " <<
                     "mine_idx : " <<
                     "(equal ? min(mine_idx, other_idx) : other_idx);\n" <<
             "}\n"
             "barrier(CLK_LOCAL_MEM_FENCE);\n" <<
        "}\n\n" <<

         // write block result to global output
        "if(lid == 0){\n" <<
            result[k.var<uint_>("get_group_id(0)")] << " = block[0];\n" <<
            result_idx[k.var<uint_>("get_group_id(0)")] << " = block_idx[0];\n" <<
        "}";

    std::string options;
    if(!find_minimum){
        options = "-DBOOST_COMPUTE_FIND_MAXIMUM";
    }
    if(use_input_idx){
        options += " -DBOOST_COMPUTE_USE_INPUT_IDX";
    }

    kernel kernel = k.compile(context, options);

    kernel.set_arg(count_arg, static_cast<uint_>(count));
    kernel.set_arg(block_arg, local_buffer<input_type>(work_group_size));
    kernel.set_arg(block_idx_arg, local_buffer<uint_>(work_group_size));

    queue.enqueue_1d_range_kernel(kernel,
                                  0,
                                  work_groups_no * work_group_size,
                                  work_group_size);
}
Example #28
0
 /// Creates a new threefry_engine and seeds it with \p value.
 explicit threefry_engine(command_queue &queue)
     : m_context(queue.get_context())
 {
     // setup program
     load_program();
 }
 void generate(OutputIterator first, OutputIterator last, Function op, command_queue &queue)
 {
     vector<T> tmp(std::distance(first, last), queue.get_context());
     generate(tmp.begin(), tmp.end(), queue);
     transform(tmp.begin(), tmp.end(), first, op, queue);
 }
Example #30
0
inline void merge_blocks_on_gpu(KeyIterator keys_first,
                                ValueIterator values_first,
                                KeyIterator out_keys_first,
                                ValueIterator out_values_first,
                                Compare compare,
                                const size_t count,
                                const size_t block_size,
                                const bool sort_by_key,
                                command_queue &queue)
{
    typedef typename std::iterator_traits<KeyIterator>::value_type key_type;
    typedef typename std::iterator_traits<ValueIterator>::value_type value_type;

    meta_kernel k("merge_blocks");
    size_t count_arg = k.add_arg<const uint_>("count");
    size_t block_size_arg = k.add_arg<const uint_>("block_size");

    k <<
        // get global id
        k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
        "if(gid >= count) {\n" <<
            "return;\n" <<
        "}\n" <<

        k.decl<const key_type>("my_key") << " = " <<
            keys_first[k.var<const uint_>("gid")] << ";\n";

    if(sort_by_key) {
        k <<
            k.decl<const value_type>("my_value") << " = " <<
                values_first[k.var<const uint_>("gid")] << ";\n";
    }

    k <<
        // get my block idx
        k.decl<const uint_>("my_block_idx") << " = gid / block_size;\n" <<
        k.decl<const bool>("my_block_idx_is_odd") << " = " <<
            "my_block_idx & 0x1;\n" <<

        k.decl<const uint_>("other_block_idx") << " = " <<
            // if(my_block_idx is odd) {} else {}
            "my_block_idx_is_odd ? my_block_idx - 1 : my_block_idx + 1;\n" <<

        // get ranges of my block and the other block
        // [my_block_start; my_block_end)
        // [other_block_start; other_block_end)
        k.decl<const uint_>("my_block_start") << " = " <<
            "min(my_block_idx * block_size, count);\n" << // including
        k.decl<const uint_>("my_block_end") << " = " <<
            "min((my_block_idx + 1) * block_size, count);\n" << // excluding

        k.decl<const uint_>("other_block_start") << " = " <<
            "min(other_block_idx * block_size, count);\n" << // including
        k.decl<const uint_>("other_block_end") << " = " <<
            "min((other_block_idx + 1) * block_size, count);\n" << // excluding

        // other block is empty, nothing to merge here
        "if(other_block_start == count){\n" <<
            out_keys_first[k.var<uint_>("gid")] << " = my_key;\n";
        if(sort_by_key) {
            k <<
                out_values_first[k.var<uint_>("gid")] << " = my_value;\n";
        }

        k <<
        "return;\n" <<
        "}\n" <<

        // lower bound
        // left_idx - lower bound
        k.decl<uint_>("left_idx") << " = other_block_start;\n" <<
        k.decl<uint_>("right_idx") << " = other_block_end;\n" <<
        "while(left_idx < right_idx) {\n" <<
            k.decl<uint_>("mid_idx") << " = (left_idx + right_idx) / 2;\n" <<
            k.decl<key_type>("mid_key") << " = " <<
                    keys_first[k.var<const uint_>("mid_idx")] << ";\n" <<
            k.decl<bool>("smaller") << " = " <<
                compare(k.var<key_type>("mid_key"),
                        k.var<key_type>("my_key")) << ";\n" <<
            "left_idx = smaller ? mid_idx + 1 : left_idx;\n" <<
            "right_idx = smaller ? right_idx :  mid_idx;\n" <<
        "}\n" <<
        // left_idx is found position in other block

        // if my_block is odd we need to get the upper bound
        "right_idx = other_block_end;\n" <<
        "if(my_block_idx_is_odd && left_idx != right_idx) {\n" <<
            k.decl<key_type>("upper_key") << " = " <<
                keys_first[k.var<const uint_>("left_idx")] << ";\n" <<
            "while(" <<
                "!(" << compare(k.var<key_type>("upper_key"),
                                k.var<key_type>("my_key")) <<
                ") && " <<
                "!(" << compare(k.var<key_type>("my_key"),
                                k.var<key_type>("upper_key")) <<
                ") && " <<
                     "left_idx < right_idx" <<
                ")" <<
            "{\n" <<
                k.decl<uint_>("mid_idx") << " = (left_idx + right_idx) / 2;\n" <<
                k.decl<key_type>("mid_key") << " = " <<
                    keys_first[k.var<const uint_>("mid_idx")] << ";\n" <<
                k.decl<bool>("equal") << " = " <<
                    "!(" << compare(k.var<key_type>("mid_key"),
                                    k.var<key_type>("my_key")) <<
                    ") && " <<
                    "!(" << compare(k.var<key_type>("my_key"),
                                    k.var<key_type>("mid_key")) <<
                    ");\n" <<
                "left_idx = equal ? mid_idx + 1 : left_idx + 1;\n" <<
                "right_idx = equal ? right_idx : mid_idx;\n" <<
                "upper_key = " <<
                    keys_first[k.var<const uint_>("left_idx")] << ";\n" <<
            "}\n" <<
        "}\n" <<

        k.decl<uint_>("offset") << " = 0;\n" <<
        "offset += gid - my_block_start;\n" <<
        "offset += left_idx - other_block_start;\n" <<
        "offset += min(my_block_start, other_block_start);\n" <<
        out_keys_first[k.var<uint_>("offset")] << " = my_key;\n";
    if(sort_by_key) {
        k <<
            out_values_first[k.var<uint_>("offset")] << " = my_value;\n";
    }

    const context &context = queue.get_context();
    ::boost::compute::kernel kernel = k.compile(context);

    const size_t work_group_size = (std::min)(
        size_t(256),
        kernel.get_work_group_info<size_t>(
            queue.get_device(), CL_KERNEL_WORK_GROUP_SIZE
        )
    );
    const size_t global_size =
        work_group_size * static_cast<size_t>(
            std::ceil(float(count) / work_group_size)
        );

    kernel.set_arg(count_arg, static_cast<uint_>(count));
    kernel.set_arg(block_size_arg, static_cast<uint_>(block_size));
    queue.enqueue_1d_range_kernel(kernel, 0, global_size, work_group_size);
}