tileMatmul

Description

This sample demonstrates how to write a matrix multiplication kernel with good performance in CUDA Tile C++. The kernel multiplies FP16 input tiles with FP32 accumulation using cuda::tiles::mma.

The sample compares a naive implementation with an optimized implementation that applies good practices and provides the compiler with additional guidance for better code generation. The host code uses CUDA events to capture execution time.

The optimized kernel uses neutral placeholder values for LOAD_LATENCY and STORE_LATENCY. See tileMatmulAutotuner for an example of autotuning these values to find a suitable configuration for your hardware.

Running

# Run with default warmup and benchmark iterations. Validation is disabled by default.
./tileMatmul

# Enable CPU validation
./tileMatmul --validate

# To run faster, skip warmups (not recommended) and set iteration to 1
./tileMatmul --skip-warmup -i 1

# Show all options
./tileMatmul --help

Command-Line Options

Example Output

Note: CPU cross-validation disabled
  matmul                                    :   0.073 ms,   115.2 GB/s, 29495.8 GFLOPS 
  matmul_naive                              :   0.497 ms,    16.9 GB/s, 4322.8 GFLOPS

Prerequisites

• CUDA Toolkit version 13.3 or later.
• CUDA Driver version 580 or later.
• Host compiler with C++20 support.