[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/thrust/examples/strided_range.cu

@@ -0,0 +1,102 @@
+#include <thrust/copy.h>
+#include <thrust/device_vector.h>
+#include <thrust/fill.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <iostream>
+
+// this example illustrates how to make strided access to a range of values
+// examples:
+//   strided_range([0, 1, 2, 3, 4, 5, 6], 1) -> [0, 1, 2, 3, 4, 5, 6]
+//   strided_range([0, 1, 2, 3, 4, 5, 6], 2) -> [0, 2, 4, 6]
+//   strided_range([0, 1, 2, 3, 4, 5, 6], 3) -> [0, 3, 6]
+//   ...
+
+template <typename Iterator>
+class strided_range
+{
+public:
+  using difference_type = typename cuda::std::iterator_traits<Iterator>::difference_type;
+
+  struct stride_functor
+  {
+    difference_type stride;
+
+    stride_functor(difference_type stride)
+        : stride(stride)
+    {}
+
+    __host__ __device__ difference_type operator()(const difference_type& i) const
+    {
+      return stride * i;
+    }
+  };
+
+  using CountingIterator    = typename thrust::counting_iterator<difference_type>;
+  using TransformIterator   = typename thrust::transform_iterator<stride_functor, CountingIterator>;
+  using PermutationIterator = typename thrust::permutation_iterator<Iterator, TransformIterator>;
+
+  // type of the strided_range iterator
+  using iterator = PermutationIterator;
+
+  // construct strided_range for the range [first,last)
+  strided_range(Iterator first, Iterator last, difference_type stride)
+      : first(first)
+      , last(last)
+      , stride(stride)
+  {}
+
+  iterator begin() const
+  {
+    return PermutationIterator(first, TransformIterator(CountingIterator(0), stride_functor(stride)));
+  }
+
+  iterator end() const
+  {
+    return begin() + ((last - first) + (stride - 1)) / stride;
+  }
+
+protected:
+  Iterator first;
+  Iterator last;
+  difference_type stride;
+};
+
+int main()
+{
+  thrust::device_vector<int> data(8);
+  data[0] = 10;
+  data[1] = 20;
+  data[2] = 30;
+  data[3] = 40;
+  data[4] = 50;
+  data[5] = 60;
+  data[6] = 70;
+  data[7] = 80;
+
+  // print the initial data
+  std::cout << "data: ";
+  thrust::copy(data.begin(), data.end(), std::ostream_iterator<int>(std::cout, " "));
+  std::cout << '\n';
+
+  using Iterator = thrust::device_vector<int>::iterator;
+
+  // create strided_range with indices [0,2,4,6]
+  strided_range<Iterator> evens(data.begin(), data.end(), 2);
+  std::cout << "sum of even indices: " << thrust::reduce(evens.begin(), evens.end()) << '\n';
+
+  // create strided_range with indices [1,3,5,7]
+  strided_range<Iterator> odds(data.begin() + 1, data.end(), 2);
+  std::cout << "sum of odd indices:  " << thrust::reduce(odds.begin(), odds.end()) << '\n';
+
+  // set odd elements to 0 with fill()
+  std::cout << "setting odd indices to zero: ";
+  thrust::fill(odds.begin(), odds.end(), 0);
+  thrust::copy(data.begin(), data.end(), std::ostream_iterator<int>(std::cout, " "));
+  std::cout << '\n';
+
+  return 0;
+}