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 * MIT License
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/*
 * COMMUNITY SAMPLE: [Brief title of what this sample demonstrates]
 *
 * Description:
 *   [Detailed explanation of what this sample demonstrates, what technique
 *    or use case it showcases, and why it's valuable]
 *
 * Requirements:
 *   - Minimum ROCm version: [e.g., ROCm 6.0+]
 *   - GPU architectures: [e.g., gfx90a, gfx942, or "All"]
 *   - Data types: [e.g., f16, f32, or "Any"]
 *   - Other: [Any other specific requirements]
 *
 * Limitations:
 *   - [List any known limitations, e.g., "Only supports square matrices"]
 *   - [Performance characteristics, if relevant]
 *   - [Any edge cases not handled]
 *
 * Author: [Optional] Your name or organization
 * Date: [Optional] Contribution date
 *
 * Note: This is a community-contributed sample provided as-is. It may not be
 * maintained with the same rigor as official samples.
 */

#include <hip/hip_runtime.h>
#include <rocwmma/rocwmma.hpp>
#include <iostream>
#include "common.hpp"

// Kernel parameters and constants
constexpr int BLOCK_DIM = 256;

// Example kernel using rocWMMA
__global__ void exampleKernel(/* your parameters here */)
{
    using namespace rocwmma;

    // TODO: Implement your rocWMMA kernel here
    // This should demonstrate the technique or use case described above
}

// Host code
int main(int argc, char** argv)
{
    // TODO: Implement host setup, memory allocation, kernel launch, and verification

    std::cout << "Community Sample: [Your sample name]" << std::endl;
    std::cout << "This sample demonstrates: [brief description]" << std::endl;

    // Initialize GPU
    hipDeviceProp_t props;
    CHECK_HIP_ERROR(hipGetDeviceProperties(&props, 0));
    std::cout << "Running on GPU: " << props.name << std::endl;

    // TODO: Your implementation here

    std::cout << "Sample completed successfully!" << std::endl;

    return 0;
}