composable_kernel/client_example/26_reduce

Client Example: Parallel Reduction (NHWC)

Theory

This client example demonstrates parallel reduction operations over NHWC tensors. Reduction is a fundamental operation in deep learning for computing statistics (such as batch mean/variance), loss aggregation, and normalization.

Mathematical Formulation: Given a tensor X[N, H, W, C] and a reduction axis (e.g., channel C):

• Sum: Y_{n,h,w} = \sum_c X_{n,h,w,c}
• Max: Y_{n,h,w} = \max_c X_{n,h,w,c}
• Mean: Y_{n,h,w} = \frac{1}{C} \sum_c X_{n,h,w,c}

Algorithmic Background:

• Reductions are implemented using parallel tree or segmented reduction algorithms.
• Efficient reductions require careful memory access, synchronization, and sometimes numerically stable algorithms.

How to Run

Prerequisites

Please follow the instructions in the main Build Guide section as a prerequisite to building and running this example.

Build and run

cd composable_kernel/client_example/26_reduce
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run (reduce over channel dimension)
./reduce_nhwc_c

Source Code Structure

Directory Layout

client_example/26_reduce/
├── reduce_nhwc_c.cpp         # Main client example: reduction over NHWC tensors (channel axis)
├── CMakeLists.txt            # Build configuration for the example

Key Functions

• main() (in reduce_nhwc_c.cpp):
  Sets up input tensors, configures reduction parameters, launches the reduction kernel, and verifies the result.
• Reduction kernel invocation:
  Uses the Composable Kernel device API to launch the reduction operation.

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Additional Details

• Supports sum, max, mean, and other reductions over NHWC tensors.
• Example parameters can be adjusted in the source for different workloads.

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Related Examples

• 12_reduce: Parallel reduction in the main example directory

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