56fd68e7dd
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
Image Processing Pipeline Example
A multi-file example showcasing the CCCL Runtime and CUB APIs working together in a semi-realistic tiled image processing pipeline.
What it does
The example generates a synthetic 65K x 65K (~4 GB) grayscale space observation on the GPU, then processes it in tiles that fit in GPU memory:
-
Pass 1 - Histogram: Upload each tile, compute per-tile histograms with
cub::DeviceHistogram, download and accumulate into a global histogram. -
Host interlude: Compute Otsu's threshold (optimal foreground/background split) and build a histogram equalization lookup table from the CDF.
-
Pass 2 - Equalize + Analyze: For each tile, apply the equalization LUT (
cub::DeviceTransform), normalize to float (cub::DeviceTransform), compute thresholded count/min/max/sum (cub::DeviceReduce::TransformReduce), and GPU-downscale a preview (cub::BlockReduce). -
Output: Write
input_preview.bmpandequalized_preview.bmp(1024 x 1024 previews). The equalized image reveals nebula structure and stars that are barely visible in the dark original.
CCCL APIs demonstrated
| File | APIs |
|---|---|
image_pipeline.h |
Shared constants and buffer/plan structs using cuda::device_buffer, cuda::host_buffer, cuda::mr::shared_resource, and spans |
detail.h |
Example-local declarations for image generation, preview output, reporting, and downscaling helpers |
detail.cu |
Synthetic image generation with cuda::launch/cuda::distribute, tile preview downscaling, cuda::copy_bytes, memory-pool statistics, and BMP output |
main.cu |
cuda::devices, cuda::device_ref, cuda::device_attributes, cuda::arch_traits_for, cuda::device_memory_pool, cuda::memory_pool_properties, cuda::mr::shared_resource, cuda::make_buffer, cuda::make_pinned_buffer, cuda::copy_bytes, cuda::copy_configuration, cuda::fill_bytes, cuda::stream, cuda::timed_event, stream.wait(...), buffer.first(), buffer.subspan(), buffer.get_unsynchronized(), CUB DeviceHistogram, DeviceTransform, DeviceReduce::TransformReduce, and BlockReduce |
CMakeLists.txt |
Standalone CMake/CPM setup for consuming CCCL from a chosen repository and tag |
Building and running
cd examples/image_pipeline
cmake -S . -B build -DCMAKE_CUDA_ARCHITECTURES=native
cmake --build build
./build/image_pipeline
The example requires CUDA Toolkit 13.1 or newer for the CCCL Runtime APIs used
by the sample. The standalone CMake project uses the vendored cmake/CPM.cmake
helper to fetch CCCL; override CCCL_REPOSITORY and CCCL_TAG to build against
a local checkout or a specific branch.
The example requires ~4 GB of pinned host memory for the full image and uses 60% of GPU memory for the per-tile working set. It should run on any GPU with at least 4 GB of memory.