This sample demonstrates the parallel binary-search algorithms exposed by cuda.compute (from the cuda-cccl package). Given a sorted d_data array and a batch of d_values to locate, one device-wide call returns the insertion index for every value:

• cuda.compute.lower_bound writes, for each value, the lowest index where it could be inserted into d_data without breaking the sort order. Equivalent to numpy.searchsorted(..., side="left").
• cuda.compute.upper_bound is the analogous upper form, equivalent to numpy.searchsorted(..., side="right").

The sample runs both algorithms on two curated inputs: one with distinct elements (where lower_bound and upper_bound agree on any value not in the data) and one with duplicates (where they diverge on present values). Results are verified against numpy.searchsorted.

• How to call cuda.compute.lower_bound / upper_bound with CuPy arrays
• The semantic difference between lower_bound and upper_bound, especially for inputs containing duplicates
• How the output dtype (np.uintp) is used for indices

• cuda.compute (from the cuda-cccl package) - device algorithms
• cuda.core - device setup
• cupy - device buffers
• numpy - host-side reference via numpy.searchsorted

• cuda.compute.lower_bound(d_data, num_items, d_values, num_values, d_out)
• cuda.compute.upper_bound(d_data, num_items, d_values, num_values, d_out)

• print_gpu_info() - print device name and compute capability

• NVIDIA GPU with Compute Capability 7.0 or higher
• Minimum GPU memory: 512 MB

• CUDA Toolkit 13.0 or newer
• Python 3.10 or newer
• cuda-cccl (>=1.0.0)
• cuda-core (>=1.0.0)
• cupy-cuda13x (>=14.0.0)

If the CUDA toolkit is not on your PATH, set CUDA_HOME so that cuda.compute's JIT path can locate its dependencies:

export CUDA_HOME=/usr/local/cuda

Install the required packages from requirements.txt:

cd /path/to/cuda-python/samples/cuda_core/binarySearch
pip install -r requirements.txt

The requirements.txt installs:

• cuda-cccl (>=1.0.0) - ships the cuda.compute module
• cuda-core (>=1.0.0)
• cupy-cuda13x (>=14.0.0)
• numpy (>=1.24.0)

cd samples/cuda_core/binarySearch
python binarySearch.py

python binarySearch.py --device 1

Device: <Your GPU Name>
Compute Capability: <X.Y>

Case 1: distinct data, mixed queries
  data    = [1, 3, 5, 7, 9]
  values  = [0, 3, 4, 10]
  lower_bound: got [0, 1, 2, 5]  expected [0, 1, 2, 5]  OK
  upper_bound: got [0, 2, 2, 5]  expected [0, 2, 2, 5]  OK

Case 2: duplicates in data
  data    = [1, 3, 3, 5, 7, 9]
  values  = [3, 3, 5, 8]
  lower_bound: got [1, 1, 3, 5]  expected [1, 1, 3, 5]  OK
  upper_bound: got [3, 3, 4, 5]  expected [3, 3, 4, 5]  OK

Done

Note: Device name and compute capability will vary based on your GPU.

• binarySearch.py - Python implementation
• README.md - This file
• requirements.txt - Sample dependencies
• ../Utilities/cuda_samples_utils.py - Common utilities (imported by this sample)