[INFRA] Import NVIDIA/CCCL upstream as optimization reference library

CCCL (CUDA C++ Core Libraries) provides:
- CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk)
- Thrust: high-level parallel algorithms (transform_reduce, sort, scan)
- libcudacxx: CUDA C++ standard library (atomics, barriers, memory)
- cudax: experimental features (memory resources, allocators)
- Tuning policies: per-SM hardware-specific algorithm parameters

Competition optimization vectors mapped to CCCL:
- Output TPS (83% weight): warp_reduce, block_reduce, device_topk
- Input TPS (14% weight): device_scan, block_load, prefetch
- Cache TPS (3% weight): prefix caching strategy patterns
- Memory (0.9 util): pooled/cached/buddy allocators

Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only)
License: Apache-2.0

cccl_upstream/c/parallel/src/segmented_reduce.cu

@@ -0,0 +1,655 @@
+//===----------------------------------------------------------------------===//
+//
+// Part of CUDA Experimental in CUDA C++ Core Libraries,
+// under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+// SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES.
+//
+//===----------------------------------------------------------------------===//
+
+#include <cub/detail/choose_offset.cuh> // cub::detail::choose_offset_t
+#include <cub/detail/launcher/cuda_driver.cuh> // cub::detail::CudaDriverLauncherFactory
+#include <cub/device/dispatch/dispatch_segmented_reduce.cuh> // cub::DispatchSegmentedReduce
+#include <cub/thread/thread_load.cuh> // cub::LoadModifier
+
+#include <cuda/__type_traits/is_trivially_copyable.h>
+
+#include <cstdlib>
+#include <cstring>
+#include <exception> // std::exception
+#include <format>
+#include <mutex>
+#include <string> // std::string
+#include <string_view> // std::string_view
+#include <type_traits> // std::is_same_v
+#include <vector> // std::format
+
+#include <stdio.h> // printf
+
+#include "jit_templates/templates/input_iterator.h"
+#include "jit_templates/templates/operation.h"
+#include "jit_templates/templates/output_iterator.h"
+#include "jit_templates/traits.h"
+#include "util/nvjitlink.h"
+#include "util/serialization.h"
+#include <cccl/c/segmented_reduce.h>
+#include <cccl/c/serialization.h>
+#include <cccl/c/types.h> // cccl_type_info
+#include <nvrtc/command_list.h>
+#include <nvrtc/ltoir_list_appender.h>
+#include <util/build_utils.h>
+#include <util/context.h>
+#include <util/errors.h>
+#include <util/indirect_arg.h>
+#include <util/types.h>
+
+struct device_segmented_reduce_policy;
+using OffsetT = unsigned long long;
+static_assert(std::is_same_v<cub::detail::choose_offset_t<OffsetT>, OffsetT>, "OffsetT must be size_t");
+
+// check we can map OffsetT to ::cuda::std::uint64_t
+static_assert(std::is_unsigned_v<OffsetT>);
+static_assert(sizeof(OffsetT) == sizeof(::cuda::std::uint64_t));
+
+namespace segmented_reduce
+{
+static cccl_type_info get_accumulator_type(cccl_op_t /*op*/, cccl_iterator_t /*input_it*/, cccl_value_t init)
+{
+  // TODO Should be decltype(op(init, *input_it)) but haven't implemented type arithmetic yet
+  //      so switching back to the old accumulator type logic for now
+  return init.type;
+}
+
+std::string get_device_segmented_reduce_kernel_name(
+  std::string_view reduction_op_t,
+  std::string_view input_iterator_t,
+  std::string_view output_iterator_t,
+  std::string_view start_offset_iterator_t,
+  std::string_view end_offset_iterator_t,
+  cccl_value_t init,
+  std::string_view accum_t)
+{
+  std::string policy_selector_t;
+  check(cccl_type_name_from_nvrtc<device_segmented_reduce_policy>(&policy_selector_t));
+
+  std::string offset_t;
+  check(cccl_type_name_from_nvrtc<OffsetT>(&offset_t));
+
+  const std::string init_t = cccl_type_enum_to_name(init.type.type);
+
+  /*
+  template <typename PolicySelector,       // 0
+            typename InputIteratorT,       // 1
+            typename OutputIteratorT,      // 2
+            typename BeginOffsetIteratorT, // 3
+            typename EndOffsetIteratorT,   // 4
+            typename OffsetT,              // 5
+            typename ReductionOpT,         // 6
+            typename InitT,                // 7
+            typename AccumT>               // 8
+   DeviceSegmentedReduceKernel(...);
+  */
+  return std::format(
+    "cub::detail::segmented_reduce::DeviceSegmentedReduceKernel<{0}, {1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}>",
+    policy_selector_t, // 0
+    input_iterator_t, // 1
+    output_iterator_t, // 2
+    start_offset_iterator_t, // 3
+    end_offset_iterator_t, // 4
+    offset_t, // 5
+    reduction_op_t, // 6
+    init_t, // 7
+    accum_t); // 8
+}
+
+struct segmented_reduce_kernel_source
+{
+  cccl_device_segmented_reduce_build_result_t& build;
+
+  CUkernel SegmentedReduceKernel() const
+  {
+    return build.segmented_reduce_kernel;
+  }
+};
+} // namespace segmented_reduce
+
+struct segmented_reduce_input_iterator_tag;
+struct segmented_reduce_output_iterator_tag;
+struct segmented_reduce_start_offset_iterator_tag;
+struct segmented_reduce_end_offset_iterator_tag;
+struct segmented_reduce_operation_tag;
+
+CUresult cccl_device_segmented_reduce_compile(
+  cccl_device_segmented_reduce_build_result_t* build_ptr,
+  cccl_iterator_t input_it,
+  cccl_iterator_t output_it,
+  cccl_iterator_t start_offset_it,
+  cccl_iterator_t end_offset_it,
+  cccl_op_t op,
+  cccl_value_t init,
+  int cc_major,
+  int cc_minor,
+  const char* cub_path,
+  const char* thrust_path,
+  const char* libcudacxx_path,
+  const char* ctk_path,
+  cccl_build_config* config)
+try
+{
+  const char* name = "device_segmented_reduce";
+
+  const cccl_type_info accum_t = segmented_reduce::get_accumulator_type(op, input_it, init);
+  const auto accum_cpp         = cccl_type_enum_to_name(accum_t.type);
+
+  const auto [input_iterator_name, input_iterator_src] =
+    get_specialization<segmented_reduce_input_iterator_tag>(template_id<input_iterator_traits>(), input_it);
+
+  const auto [output_iterator_name, output_iterator_src] =
+    get_specialization<segmented_reduce_output_iterator_tag>(template_id<output_iterator_traits>(), output_it, accum_t);
+
+  const auto [start_offset_iterator_name, start_offset_iterator_src] =
+    get_specialization<segmented_reduce_start_offset_iterator_tag>(
+      template_id<input_iterator_traits>(), start_offset_it);
+
+  const auto [end_offset_iterator_name, end_offset_iterator_src] =
+    get_specialization<segmented_reduce_end_offset_iterator_tag>(template_id<input_iterator_traits>(), end_offset_it);
+
+  const auto [op_name, op_src] = get_specialization<segmented_reduce_operation_tag>(
+    template_id<binary_user_operation_traits>(), op, accum_t, accum_t, accum_t);
+
+  // OffsetT is checked to match have 64-bit size
+  const auto offset_t = cccl_type_enum_to_name(cccl_type_enum::CCCL_UINT64);
+
+  const auto policy_sel = [&] {
+    using namespace cub::detail;
+
+    const auto accum_type  = cccl_type_enum_to_cub_type(accum_t.type);
+    const auto operation_t = cccl_op_kind_to_cub_op(op.type);
+
+    const int offset_size = int{sizeof(OffsetT)};
+    return cub::detail::segmented_reduce::policy_selector{
+      accum_type, operation_t, offset_size, static_cast<int>(accum_t.size)};
+  }();
+
+  // TODO(bgruber): drop this if tuning policies become formattable
+  std::stringstream policy_sel_str;
+  policy_sel_str << policy_sel(cuda::compute_capability{cc_major, cc_minor});
+
+  const auto policy_sel_expr =
+    std::format("cub::detail::segmented_reduce::policy_selector_from_types<{}, {}, {}>", accum_cpp, offset_t, op_name);
+
+  const auto final_src = std::format(
+    R"XXX(
+#include <cub/block/block_reduce.cuh>
+#include <cub/device/dispatch/tuning/tuning_segmented_reduce.cuh>
+#include <cub/device/dispatch/kernels/kernel_segmented_reduce.cuh>
+{0}
+struct __align__({2}) storage_t {{
+  char data[{1}];
+}};
+{3}
+{4}
+{5}
+{6}
+{7}
+using device_segmented_reduce_policy = {8};
+using namespace cub;
+using namespace cub::detail::reduce;
+using namespace cub::detail::segmented_reduce;
+static_assert(
+  device_segmented_reduce_policy()(detail::current_tuning_cc()) == {9},
+  "Host generated and JIT compiled policy mismatch");
+)XXX",
+    jit_template_header_contents, // 0
+    input_it.value_type.size, // 1
+    input_it.value_type.alignment, // 2
+    input_iterator_src, // 3
+    output_iterator_src, // 4
+    op_src, // 5
+    start_offset_iterator_src, // 6
+    end_offset_iterator_src, // 7
+    policy_sel_expr, // 8
+    policy_sel_str.view()); // 9
+
+#if false // CCCL_DEBUGGING_SWITCH
+  fflush(stderr);
+  printf("\nCODE4NVRTC BEGIN\n%sCODE4NVRTC END\n", final_src.c_str());
+  fflush(stdout);
+#endif
+
+  std::string segmented_reduce_kernel_name = segmented_reduce::get_device_segmented_reduce_kernel_name(
+    op_name,
+    input_iterator_name,
+    output_iterator_name,
+    start_offset_iterator_name,
+    end_offset_iterator_name,
+    init,
+    accum_cpp);
+  std::string segmented_reduce_kernel_lowered_name;
+
+  const std::string arch = std::format("-arch=sm_{0}{1}", cc_major, cc_minor);
+
+  std::vector<const char*> args = {
+    arch.c_str(),
+    cub_path,
+    thrust_path,
+    libcudacxx_path,
+    ctk_path,
+    "-rdc=true",
+    "-dlto",
+    "-DCUB_DISABLE_CDP",
+    "-std=c++20",
+    "-default-device"};
+
+  cccl::detail::extend_args_with_build_config(args, config);
+
+  constexpr size_t num_lto_args   = 2;
+  const char* lopts[num_lto_args] = {"-lto", arch.c_str()};
+
+  const bool kernel_only = is_custom_op(op);
+
+  // Collect all LTO-IRs to be linked (empty in kernel-only mode).
+  nvrtc_linkable_list linkable_list;
+  nvrtc_linkable_list_appender appender{linkable_list};
+
+  appender.append_operation(op);
+  appender.add_iterator_definition(input_it);
+  appender.add_iterator_definition(output_it);
+  appender.add_iterator_definition(start_offset_it);
+  appender.add_iterator_definition(end_offset_it);
+
+  auto post_build =
+    begin_linking_nvrtc_program(kernel_only ? 0 : num_lto_args, kernel_only ? nullptr : lopts)
+      ->add_program(nvrtc_translation_unit{final_src.c_str(), name})
+      ->add_expression({segmented_reduce_kernel_name})
+      ->compile_program({args.data(), args.size()})
+      ->get_name({segmented_reduce_kernel_name, segmented_reduce_kernel_lowered_name});
+
+  struct free_deleter
+  {
+    void operator()(void* p) const
+    {
+      std::free(p);
+    }
+  };
+  static_assert(::cuda::is_trivially_copyable_v<cub::detail::segmented_reduce::policy_selector>);
+  const size_t policy_size = sizeof(policy_sel);
+  std::unique_ptr<void, free_deleter> policy_ptr(std::malloc(policy_size));
+  if (!policy_ptr)
+  {
+    return CUDA_ERROR_OUT_OF_MEMORY;
+  }
+  std::memcpy(policy_ptr.get(), &policy_sel, sizeof(policy_sel));
+  auto kernel_name = std::unique_ptr<char[]>(duplicate_c_string(segmented_reduce_kernel_lowered_name));
+
+  build_ptr->cc               = cc_major * 10 + cc_minor;
+  build_ptr->accumulator_size = accum_t.size;
+  // Zero-init fields set by _load, not _compile.
+  build_ptr->library                 = nullptr;
+  build_ptr->segmented_reduce_kernel = nullptr;
+
+  // All potentially-throwing operations come before any release() calls so that
+  // unique_ptrs automatically clean up on exception.
+  if (kernel_only)
+  {
+    auto [ltoir_size, ltoir_data] = post_build->get_program_ltoir();
+    build_ptr->payload            = ltoir_data.release();
+    build_ptr->payload_size       = ltoir_size;
+    build_ptr->payload_kind       = CCCL_PAYLOAD_LTOIR;
+  }
+  else
+  {
+    nvrtc_link_result result = post_build->link_program()->add_link_list(linkable_list)->finalize_program();
+    build_ptr->payload       = (void*) result.data.release();
+    build_ptr->payload_size  = result.size;
+    build_ptr->payload_kind  = CCCL_PAYLOAD_CUBIN;
+  }
+
+  build_ptr->runtime_policy                       = policy_ptr.release();
+  build_ptr->runtime_policy_size                  = policy_size;
+  build_ptr->segmented_reduce_kernel_lowered_name = kernel_name.release();
+
+  return CUDA_SUCCESS;
+}
+catch (const std::exception& exc)
+{
+  fflush(stderr);
+  printf("\nEXCEPTION in cccl_device_segmented_reduce_compile(): %s\n", exc.what());
+  fflush(stdout);
+
+  return CUDA_ERROR_UNKNOWN;
+}
+
+CUresult cccl_device_segmented_reduce_load(cccl_device_segmented_reduce_build_result_t* build_ptr)
+try
+{
+  if (build_ptr == nullptr || build_ptr->payload == nullptr || build_ptr->payload_size == 0
+      || build_ptr->payload_kind != CCCL_PAYLOAD_CUBIN || build_ptr->segmented_reduce_kernel_lowered_name == nullptr
+      || build_ptr->segmented_reduce_kernel_lowered_name[0] == '\0')
+  {
+    return CUDA_ERROR_INVALID_VALUE;
+  }
+  CUresult status =
+    cuLibraryLoadData(&build_ptr->library, build_ptr->payload, nullptr, nullptr, 0, nullptr, nullptr, 0);
+  if (status != CUDA_SUCCESS)
+  {
+    return status;
+  }
+  try
+  {
+    check(cuLibraryGetKernel(
+      &build_ptr->segmented_reduce_kernel, build_ptr->library, build_ptr->segmented_reduce_kernel_lowered_name));
+  }
+  catch (...)
+  {
+    cuLibraryUnload(build_ptr->library);
+    build_ptr->library = nullptr;
+    throw;
+  }
+  return CUDA_SUCCESS;
+}
+catch (const std::exception& exc)
+{
+  fflush(stderr);
+  printf("\nEXCEPTION in cccl_device_segmented_reduce_load(): %s\n", exc.what());
+  fflush(stdout);
+  return CUDA_ERROR_UNKNOWN;
+}
+
+CUresult cccl_device_segmented_reduce_build_ex(
+  cccl_device_segmented_reduce_build_result_t* build_ptr,
+  cccl_iterator_t input_it,
+  cccl_iterator_t output_it,
+  cccl_iterator_t start_offset_it,
+  cccl_iterator_t end_offset_it,
+  cccl_op_t op,
+  cccl_value_t init,
+  int cc_major,
+  int cc_minor,
+  const char* cub_path,
+  const char* thrust_path,
+  const char* libcudacxx_path,
+  const char* ctk_path,
+  cccl_build_config* config)
+{
+  CUresult r = cccl_device_segmented_reduce_compile(
+    build_ptr,
+    input_it,
+    output_it,
+    start_offset_it,
+    end_offset_it,
+    op,
+    init,
+    cc_major,
+    cc_minor,
+    cub_path,
+    thrust_path,
+    libcudacxx_path,
+    ctk_path,
+    config);
+  if (r != CUDA_SUCCESS)
+  {
+    return r;
+  }
+  CUresult load_r = cccl_device_segmented_reduce_load(build_ptr);
+  if (load_r != CUDA_SUCCESS)
+  {
+    cccl_device_segmented_reduce_cleanup(build_ptr);
+  }
+  return load_r;
+}
+
+CUresult cccl_device_segmented_reduce(
+  cccl_device_segmented_reduce_build_result_t build,
+  void* d_temp_storage,
+  size_t* temp_storage_bytes,
+  cccl_iterator_t d_in,
+  cccl_iterator_t d_out,
+  uint64_t num_segments,
+  cccl_iterator_t start_offset,
+  cccl_iterator_t end_offset,
+  cccl_op_t op,
+  cccl_value_t init,
+  size_t max_segment_size,
+  CUstream stream)
+{
+  bool pushed    = false;
+  CUresult error = CUDA_SUCCESS;
+  try
+  {
+    pushed = try_push_context();
+
+    CUdevice cu_device;
+    check(cuCtxGetDevice(&cu_device));
+
+    auto exec_status = cub::detail::segmented_reduce::dispatch</* OverrideAccumT */ void, OffsetT>(
+      d_temp_storage,
+      *temp_storage_bytes,
+      indirect_arg_t{d_in},
+      indirect_iterator_t{d_out},
+      num_segments,
+      indirect_iterator_t{start_offset},
+      indirect_iterator_t{end_offset},
+      indirect_arg_t{op},
+      indirect_arg_t{init},
+      max_segment_size,
+      stream,
+      *static_cast<cub::detail::segmented_reduce::policy_selector*>(build.runtime_policy),
+      segmented_reduce::segmented_reduce_kernel_source{build},
+      cub::detail::CudaDriverLauncherFactory{cu_device, build.cc});
+
+    error = static_cast<CUresult>(exec_status);
+  }
+  catch (const std::exception& exc)
+  {
+    fflush(stderr);
+    printf("\nEXCEPTION in cccl_device_segmented_reduce(): %s\n", exc.what());
+    fflush(stdout);
+    error = CUDA_ERROR_UNKNOWN;
+  }
+
+  if (pushed)
+  {
+    CUcontext dummy;
+    cuCtxPopCurrent(&dummy);
+  }
+
+  return error;
+}
+
+CUresult cccl_device_segmented_reduce_build(
+  cccl_device_segmented_reduce_build_result_t* build,
+  cccl_iterator_t d_in,
+  cccl_iterator_t d_out,
+  cccl_iterator_t begin_offset_in,
+  cccl_iterator_t end_offset_in,
+  cccl_op_t op,
+  cccl_value_t init,
+  int cc_major,
+  int cc_minor,
+  const char* cub_path,
+  const char* thrust_path,
+  const char* libcudacxx_path,
+  const char* ctk_path)
+{
+  return cccl_device_segmented_reduce_build_ex(
+    build,
+    d_in,
+    d_out,
+    begin_offset_in,
+    end_offset_in,
+    op,
+    init,
+    cc_major,
+    cc_minor,
+    cub_path,
+    thrust_path,
+    libcudacxx_path,
+    ctk_path,
+    nullptr);
+}
+
+CUresult cccl_device_segmented_reduce_cleanup(cccl_device_segmented_reduce_build_result_t* build_ptr)
+try
+{
+  if (build_ptr == nullptr)
+  {
+    return CUDA_ERROR_INVALID_VALUE;
+  }
+
+  std::unique_ptr<char[]> payload(reinterpret_cast<char*>(build_ptr->payload));
+  std::free(build_ptr->runtime_policy);
+  std::unique_ptr<char[]> kernel_name(build_ptr->segmented_reduce_kernel_lowered_name);
+  if (build_ptr->library != nullptr)
+  {
+    check(cuLibraryUnload(build_ptr->library));
+  }
+
+  return CUDA_SUCCESS;
+}
+catch (const std::exception& exc)
+{
+  fflush(stderr);
+  printf("\nEXCEPTION in cccl_device_segmented_reduce_cleanup(): %s\n", exc.what());
+  fflush(stdout);
+
+  return CUDA_ERROR_UNKNOWN;
+}
+
+CUresult cccl_device_segmented_reduce_link_ltoir(
+  cccl_device_segmented_reduce_build_result_t* build_ptr,
+  const void** input_blobs,
+  const size_t* input_sizes,
+  size_t num_inputs)
+try
+{
+  if (build_ptr == nullptr || build_ptr->payload == nullptr || build_ptr->payload_size == 0
+      || build_ptr->payload_kind != CCCL_PAYLOAD_LTOIR)
+  {
+    return CUDA_ERROR_INVALID_VALUE;
+  }
+  const int cc_major = build_ptr->cc / 10;
+  const int cc_minor = build_ptr->cc % 10;
+  std::vector<const void*> all_blobs;
+  std::vector<size_t> all_sizes;
+  all_blobs.push_back(build_ptr->payload);
+  all_sizes.push_back(build_ptr->payload_size);
+  if (num_inputs > 0 && (input_blobs == nullptr || input_sizes == nullptr))
+  {
+    return CUDA_ERROR_INVALID_VALUE;
+  }
+  for (size_t i = 0; i < num_inputs; ++i)
+  {
+    if (input_blobs[i] == nullptr || input_sizes[i] == 0)
+    {
+      return CUDA_ERROR_INVALID_VALUE;
+    }
+    all_blobs.push_back(input_blobs[i]);
+    all_sizes.push_back(input_sizes[i]);
+  }
+  auto [cubin, cubin_size] = nvjitlink_link(all_blobs.data(), all_sizes.data(), all_blobs.size(), cc_major, cc_minor);
+  delete[] static_cast<char*>(build_ptr->payload);
+  build_ptr->payload      = (void*) cubin.release();
+  build_ptr->payload_size = cubin_size;
+  build_ptr->payload_kind = CCCL_PAYLOAD_CUBIN;
+  return CUDA_SUCCESS;
+}
+catch (const std::exception& exc)
+{
+  printf("\nEXCEPTION in cccl_device_segmented_reduce_link_ltoir(): %s\n", exc.what());
+  return CUDA_ERROR_UNKNOWN;
+}
+
+CUresult cccl_device_segmented_reduce_serialize(
+  const cccl_device_segmented_reduce_build_result_t* build_ptr, void** out_buf, size_t* out_size)
+try
+{
+  if (build_ptr == nullptr || out_buf == nullptr || out_size == nullptr)
+  {
+    return CUDA_ERROR_INVALID_VALUE;
+  }
+  if (build_ptr->payload == nullptr || build_ptr->payload_size == 0 || build_ptr->runtime_policy == nullptr
+      || build_ptr->runtime_policy_size == 0)
+  {
+    *out_buf  = nullptr;
+    *out_size = 0;
+    return CUDA_ERROR_INVALID_VALUE;
+  }
+
+  using namespace cccl::serialization;
+  buffer_writer w;
+  write_header(w, CCCL_SERIALIZATION_ALGO_SEGMENTED_REDUCE, build_ptr->payload_kind, build_ptr->cc);
+  w.write_pod<uint64_t>(build_ptr->accumulator_size);
+  w.write_blob(build_ptr->payload, build_ptr->payload_size);
+  w.write_blob(build_ptr->runtime_policy, build_ptr->runtime_policy_size);
+  w.write_cstring(build_ptr->segmented_reduce_kernel_lowered_name);
+  w.release(out_buf, out_size);
+  return CUDA_SUCCESS;
+}
+catch (const std::exception& exc)
+{
+  fflush(stderr);
+  printf("\nEXCEPTION in cccl_device_segmented_reduce_serialize(): %s\n", exc.what());
+  fflush(stdout);
+  return CUDA_ERROR_UNKNOWN;
+}
+
+CUresult cccl_device_segmented_reduce_deserialize(
+  cccl_device_segmented_reduce_build_result_t* build_ptr, const void* buf, size_t size)
+try
+{
+  if (build_ptr == nullptr || buf == nullptr || size == 0)
+  {
+    return CUDA_ERROR_INVALID_VALUE;
+  }
+
+  using namespace cccl::serialization;
+  buffer_reader r{buf, size};
+  const auto h = read_and_validate_header(r, CCCL_SERIALIZATION_ALGO_SEGMENTED_REDUCE);
+
+  const uint64_t accum_size = r.read_pod<uint64_t>();
+
+  std::unique_ptr<char[]> payload_owner;
+  size_t payload_size = 0;
+  {
+    void* p = nullptr;
+    r.read_blob_new(&p, &payload_size);
+    payload_owner.reset(static_cast<char*>(p));
+  }
+  if (payload_size == 0)
+  {
+    throw std::runtime_error("serialization blob: empty payload");
+  }
+
+  std::unique_ptr<cub::detail::segmented_reduce::policy_selector, decltype(&std::free)> policy(
+    static_cast<cub::detail::segmented_reduce::policy_selector*>(
+      std::malloc(sizeof(cub::detail::segmented_reduce::policy_selector))),
+    std::free);
+  if (!policy)
+  {
+    return CUDA_ERROR_OUT_OF_MEMORY;
+  }
+  r.read_into(policy.get(), sizeof(cub::detail::segmented_reduce::policy_selector));
+
+  std::unique_ptr<char[]> n_kernel{r.read_cstring_dup()};
+
+  cccl_device_segmented_reduce_build_result_t result{};
+  result.cc                                   = static_cast<int>(h.cc);
+  result.payload_kind                         = static_cast<cccl_payload_kind_t>(h.payload_kind);
+  result.accumulator_size                     = accum_size;
+  result.payload                              = payload_owner.release();
+  result.payload_size                         = payload_size;
+  result.runtime_policy                       = policy.release();
+  result.runtime_policy_size                  = sizeof(cub::detail::segmented_reduce::policy_selector);
+  result.segmented_reduce_kernel_lowered_name = n_kernel.release();
+  *build_ptr                                  = result;
+  return CUDA_SUCCESS;
+}
+catch (const std::exception& exc)
+{
+  fflush(stderr);
+  printf("\nEXCEPTION in cccl_device_segmented_reduce_deserialize(): %s\n", exc.what());
+  fflush(stdout);
+  return CUDA_ERROR_UNKNOWN;
+}