if.cu

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#include <cub/device/device_select.cuh>

#include <thrust/count.h>

#include <limits>

#include <look_back_helper.cuh>
#include <nvbench_helper.cuh>

// %RANGE% TUNE_TRANSPOSE trp 0:1:1
// %RANGE% TUNE_LOAD ld 0:1:1
// %RANGE% TUNE_ITEMS_PER_THREAD ipt 7:24:1
// %RANGE% TUNE_THREADS_PER_BLOCK tpb 128:1024:32
// %RANGE% TUNE_MAGIC_NS ns 0:2048:4
// %RANGE% TUNE_DELAY_CONSTRUCTOR_ID dcid 0:7:1
// %RANGE% TUNE_L2_WRITE_LATENCY_NS l2w 0:1200:5

constexpr bool keep_rejects = false;

#if !TUNE_BASE
#  if TUNE_TRANSPOSE == 0
#    define TUNE_LOAD_ALGORITHM cub::BLOCK_LOAD_DIRECT
#  else // TUNE_TRANSPOSE == 1
#    define TUNE_LOAD_ALGORITHM cub::BLOCK_LOAD_WARP_TRANSPOSE
#  endif // TUNE_TRANSPOSE

#  if TUNE_LOAD == 0
#    define TUNE_LOAD_MODIFIER cub::LOAD_DEFAULT
#  else // TUNE_LOAD == 1
#    define TUNE_LOAD_MODIFIER cub::LOAD_CA
#  endif // TUNE_LOAD

template <typename InputT>
struct policy_hub_t
{
  struct policy_t : cub::ChainedPolicy<300, policy_t, policy_t>
  {
    static constexpr int NOMINAL_4B_ITEMS_PER_THREAD = TUNE_ITEMS_PER_THREAD;

    static constexpr int ITEMS_PER_THREAD =
      CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(InputT))));

    using SelectIfPolicyT =
      cub::AgentSelectIfPolicy<TUNE_THREADS_PER_BLOCK,
                               ITEMS_PER_THREAD,
                               TUNE_LOAD_ALGORITHM,
                               TUNE_LOAD_MODIFIER,
                               cub::BLOCK_SCAN_WARP_SCANS,
                               delay_constructor_t>;
  };

  using MaxPolicy = policy_t;
};
#endif // !TUNE_BASE

template <class T>
struct less_then_t
{
  T m_val;

  __device__ bool operator()(const T& val) const
  {
    return val < m_val;
  }
};

template <typename T>
T value_from_entropy(double percentage)
{
  if (percentage == 1)
  {
    return std::numeric_limits<T>::max();
  }

  const auto max_val = static_cast<double>(std::numeric_limits<T>::max());
  const auto min_val = static_cast<double>(std::numeric_limits<T>::lowest());
  const auto result  = min_val + percentage * max_val - percentage * min_val;
  return static_cast<T>(result);
}

template <typename T, typename OffsetT, typename MayAlias>
void select(nvbench::state& state, nvbench::type_list<T, OffsetT, MayAlias>)
{
  using input_it_t         = const T*;
  using flag_it_t          = cub::NullType*;
  using output_it_t        = T*;
  using num_selected_it_t  = OffsetT*;
  using select_op_t        = less_then_t<T>;
  using equality_op_t      = cub::NullType;
  using offset_t           = OffsetT;
  constexpr bool may_alias = MayAlias::value;

#if !TUNE_BASE
  using policy_t   = policy_hub_t<T>;
  using dispatch_t = cub::DispatchSelectIf<
    input_it_t,
    flag_it_t,
    output_it_t,
    num_selected_it_t,
    select_op_t,
    equality_op_t,
    offset_t,
    keep_rejects,
    may_alias,
    policy_t>;
#else // TUNE_BASE
  using dispatch_t = cub::DispatchSelectIf<
    input_it_t,
    flag_it_t,
    output_it_t,
    num_selected_it_t,
    select_op_t,
    equality_op_t,
    offset_t,
    keep_rejects,
    may_alias>;
#endif // TUNE_BASE

  // Retrieve axis parameters
  const auto elements       = static_cast<std::size_t>(state.get_int64("Elements{io}"));
  const bit_entropy entropy = str_to_entropy(state.get_string("Entropy"));

  T val = value_from_entropy<T>(entropy_to_probability(entropy));
  select_op_t select_op{val};

  thrust::device_vector<T> in = generate(elements);
  thrust::device_vector<offset_t> num_selected(1);

  // TODO Extract into helper TU
  const auto selected_elements = thrust::count_if(in.cbegin(), in.cend(), select_op);
  thrust::device_vector<T> out(selected_elements);

  input_it_t d_in                  = thrust::raw_pointer_cast(in.data());
  output_it_t d_out                = thrust::raw_pointer_cast(out.data());
  flag_it_t d_flags                = nullptr;
  num_selected_it_t d_num_selected = thrust::raw_pointer_cast(num_selected.data());

  state.add_element_count(elements);
  state.add_global_memory_reads<T>(elements);
  state.add_global_memory_writes<T>(selected_elements);
  state.add_global_memory_writes<offset_t>(1);

  std::size_t temp_size{};
  dispatch_t::Dispatch(
    nullptr, temp_size, d_in, d_flags, d_out, d_num_selected, select_op, equality_op_t{}, elements, 0);

  thrust::device_vector<nvbench::uint8_t> temp(temp_size);
  auto* temp_storage = thrust::raw_pointer_cast(temp.data());

  state.exec(nvbench::exec_tag::no_batch, [&](nvbench::launch& launch) {
    dispatch_t::Dispatch(
      temp_storage,
      temp_size,
      d_in,
      d_flags,
      d_out,
      d_num_selected,
      select_op,
      equality_op_t{},
      elements,
      launch.get_stream());
  });
}

using ::cuda::std::false_type;
using ::cuda::std::true_type;
#ifdef TUNE_MayAlias
using may_alias = nvbench::type_list<TUNE_MayAlias>; // expands to "false_type" or "true_type"
#else // !defined(TUNE_MayAlias)
using may_alias = nvbench::type_list<false_type, true_type>;
#endif // TUNE_MayAlias

// The implementation of DeviceSelect for 64-bit offset types uses a streaming approach, where it runs multiple passes
// using a 32-bit offset type, so we only need to test one (to save time for tuning and the benchmark CI).
using select_offset_types = nvbench::type_list<int64_t>;

NVBENCH_BENCH_TYPES(select, NVBENCH_TYPE_AXES(fundamental_types, select_offset_types, may_alias))
  .set_name("base")
  .set_type_axes_names({"T{ct}", "OffsetT{ct}", "MayAlias{ct}"})
  .add_int64_power_of_two_axis("Elements{io}", nvbench::range(16, 28, 4))
  .add_string_axis("Entropy", {"1.000", "0.544", "0.000"});