cuda-samples/python/2_CoreConcepts/cudaComputeLambdas

cudaComputeLambdas (Python)

Description

This sample demonstrates how cuda.compute (from the cuda-cccl package) accepts plain Python callables, including lambdas, as the operators that drive device-wide reductions, transforms, and scans. Internally cuda.compute JIT-compiles the callable through Numba for the GPU, so you can iterate on the operator in pure Python and still get a fused device-wide kernel.

The sample exercises three algorithm families:

1. cuda.compute.reduce_into - sum via lambda a, b: a + b.
2. cuda.compute.unary_transform - elementwise y = x*x + 1 via a lambda.
3. cuda.compute.inclusive_scan - prefix sum over only the even values, driven by a regular Python function as the binary operator.

What You'll Learn

• Passing a Python lambda directly as the operator to a cuda.compute device algorithm
• Using a regular Python def function for the same purpose when the op is non-trivial
• The three core algorithm families in cuda.compute: reductions, transforms, and scans
• How cuda.compute auto-compiles the op to LTO-IR via Numba

Key Libraries

• cuda.compute (from the cuda-cccl package) - device algorithms and JIT-compiled Python ops
• cuda.core - device setup
• cupy - device buffers
• numpy - scalar init values and host-side verification

Key APIs

From cuda.compute

• cuda.compute.reduce_into(d_in, d_out, num_items, op, h_init) - device-wide reduction
• cuda.compute.unary_transform(d_in, d_out, num_items, op) - elementwise unary transform
• cuda.compute.inclusive_scan(d_in, d_out, op, init_value, num_items) - inclusive prefix scan

From cuda_samples_utils

• print_gpu_info() - print device name and compute capability

Requirements

Hardware

• NVIDIA GPU with Compute Capability 7.0 or higher

Software

• CUDA Toolkit 13.0 or newer (cuda.compute compiles ops to LTO-IR via Numba, which needs the toolkit's nvvm and libdevice).
• Python 3.10 or newer
• cuda-cccl (>=1.0.0)
• cuda-core (>=1.0.0)
• cupy-cuda13x (>=14.0.0)
• numba-cuda (pulled in transitively by cuda-cccl)

If the CUDA toolkit is not on your PATH, set CUDA_HOME so Numba can locate libdevice:

export CUDA_HOME=/usr/local/cuda

Installation

Install the required packages from requirements.txt:

cd /path/to/cuda-samples/python/2_CoreConcepts/cudaComputeLambdas
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)

How to Run

Basic usage

cd cuda-samples/python/2_CoreConcepts/cudaComputeLambdas
python cudaComputeLambdas.py

With custom parameters

python cudaComputeLambdas.py --device 1

Expected Output

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

reduce_into(lambda a,b: a+b) over 1..10 -> 55 (expected 55)  OK

unary_transform(lambda x: x*x + 1):
  got      = [1, 2, 5, 10, 17, 26, 37, 50]
  expected = [1, 2, 5, 10, 17, 26, 37, 50]  OK

inclusive_scan(add-evens-only) over [1,2,3,4,5,6]:
  got      = [0, 2, 2, 6, 6, 12]
  expected = [0, 2, 2, 6, 6, 12]  OK

Done

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

Files

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