Creating a custom CUDA kernel that directly utilizes tensor cores

Creating a custom CUDA kernel that directly utilizes tensor cores is an advanced topic, as tensor cores are typically accessed through higher-level libraries like cuBLAS or cuDNN. However, NVIDIA does provide a way to use tensor cores in custom kernels through their CUDA Core library, specifically with Warp Matrix Multiply-Accumulate (WMMA) API. Here's an overview of how to create a kernel that works on tensor cores:

1. Use CUDA Core WMMA API:

The WMMA API allows you to program tensor cores directly in your CUDA kernels.

2. Include necessary headers:

```cpp

#include <mma.h>

#include <cuda_fp16.h>

```

3. Use appropriate data types:

Tensor cores work with specific data types like half precision floating point (`__half`).

4. Define matrix fragments:

Use `nvcuda::wmma::fragment` to define matrix fragments that will be processed by tensor cores.

5. Load, compute, and store operations:

Use WMMA load, multiply-accumulate, and store operations.

Here's an example kernel that uses tensor cores via the WMMA API:

```cuda

#include <mma.h>

#include <cuda_fp16.h>

// Define matrix dimensions

const int M = 16;

const int N = 16;

const int K = 16;

__global__ void wmma_example(half *a, half *b, float *c) {

// Declare the fragments

nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, M, N, K, half, nvcuda::wmma::col_major> a_frag;

nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, M, N, K, half, nvcuda::wmma::col_major> b_frag;

nvcuda::wmma::fragment<nvcuda::wmma::accumulator, M, N, K, float> c_frag;

// Initialize the output to zero

nvcuda::wmma::fill_fragment(c_frag, 0.0f);

// Load the inputs

nvcuda::wmma::load_matrix_sync(a_frag, a, K);

nvcuda::wmma::load_matrix_sync(b_frag, b, K);

// Perform the matrix multiplication

nvcuda::wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);

// Store the output

nvcuda::wmma::store_matrix_sync(c, c_frag, N, nvcuda::wmma::mem_row_major);

}

// Host function to launch the kernel

void launch_wmma_kernel(half *a, half *b, float *c) {

dim3 gridDim(1);

dim3 blockDim(32); // One warp

wmma_example<<<gridDim, blockDim>>>(a, b, c);

}

```

Key points about this example:

1. We're using 16x16 matrices as this is a common size for tensor core operations.
2. The kernel uses `nvcuda::wmma::fragment` to define matrix fragments.
3. `load_matrix_sync`, `mma_sync`, and `store_matrix_sync` are used to load data, perform matrix multiplication, and store results using tensor cores.
4. The kernel operates on half-precision input (`half`) and produces single-precision output (`float`).

To use this kernel:

1. Compile with a CUDA compiler that supports tensor cores (CUDA 9.0 or later).
2. Use appropriate GPU architecture flags (e.g., `-arch=sm_70` for Volta, `-arch=sm_75` for Turing).
3. Allocate memory and copy data to the GPU before calling `launch_wmma_kernel`.

Important considerations:

1. Error checking is omitted for brevity but should be included in production code.
2. This is a basic example. Real-world usage often involves tiling and more complex memory access patterns for larger matrices.
3. Performance tuning is crucial. The exact dimensions and data types should be chosen based on your specific use case and target GPU architecture.
4. Not all operations can be efficiently mapped to tensor cores. They're most beneficial for large matrix multiplications common in deep learning workloads.

Remember, while this approach gives you direct control over tensor core usage, in many cases, using higher-level libraries like cuBLAS or cuDNN is more practical and can automatically leverage tensor cores when appropriate.