User defined Kernels

Overview

Tutorial: 30 min

Objectives:

Learn about Elementwise Kernels.

Learn about Reduction Kernels.

Learn about Raw Kernels.

Elementwise Kernel

CuPy’s ElementwiseKernel allows you to define custom element-wise operations that are executed on the GPU. This is useful for operations that are not covered by CuPy’s built-in functions.

Explanation of `ElementwiseKernel`:

Input and Output Types: - The first argument specifies the input types. In this case, ‘float32 x, float32 y’ means that the kernel takes two input arrays of type float32. - The second argument specifies the output type. Here, ‘float32 z’ means that the output array will be of type float32.
Kernel Code: - The third argument is the kernel code itself. This is a string containing the operation to be performed element-wise. In this example, ‘z = (x - y)’ means that for each element, the value of z will be the difference between the corresponding elements of x and y.
Kernel Name: - The fourth argument is the name of the kernel. This is useful for debugging and profiling. In this case, the kernel is named ‘element_diff’.

Sample Code:

element_diff = cp.ElementwiseKernel('float32 x, float32 y',
                                    'float32 z',
                                    'z = (x - y)',
                                    'element_diff')

Reduction Kernel

A reduction kernel in CuPy is used to perform reduction operations on arrays. Reduction operations are those that reduce an array to a single value, such as summing all elements or finding the maximum value. The cp.ReductionKernel function allows you to define custom reduction operations.

Here’s a breakdown of the parameters used in the example:

‘T x’: The input parameter type and name.
‘T y’: The output parameter type and name.
‘x’: The map expression, which is applied to each element of the input array.
‘a + b’: The reduction expression, which combines two elements.
‘y = a’: The post-reduction map expression, which is applied to the result of the reduction.
‘0’: The identity value for the reduction operation.
‘reduction_kernel’: The name of the kernel.

import cupy as cp

reduction_kernel = cp.ReductionKernel(
    'T x',  # input param
    'T y',  # output param
    'x',  # map
    'a + b',  # reduce
    'y = a',  # post-reduction map
    '0',  # identity value
    'reduction_kernel'  # kernel name
)

# Example usage
x = cp.array([1, 2, 3, 4, 5], dtype=cp.float32)
result = reduction_kernel(x)
print(result)  # Output: 15.0

Raw Kernel

CuPy’s RawKernel allows you to define custom CUDA kernels using CUDA C/C++ code. This is useful for operations that are not covered by CuPy’s built-in functions or when you need more control over the GPU execution.

Explanation of `RawKernel`:

RawKernel Definition:
```python add_kernel = cp.RawKernel(r’’’ extern “C” __global__ void my_add(const float* x1, const float* x2, float* y) {

int tid = blockDim.x * blockIdx.x + threadIdx.x; y[tid] = x1[tid] + x2[tid];

}’’’, ‘my_add’) ``` - cp.RawKernel: This is a CuPy class that allows you to define a raw CUDA kernel. - r’’’ … ‘’’: This is a raw string literal in Python, which allows you to include the CUDA C code without escaping backslashes. - ‘my_add’: This is the name of the kernel function defined in the CUDA C code.
CUDA C Code:
```c extern “C” __global__ void my_add(const float* x1, const float* x2, float* y) {

int tid = blockDim.x * blockIdx.x + threadIdx.x; y[tid] = x1[tid] + x2[tid];
- extern “C”: This specifies that the function should use C linkage, which is necessary for interoperability between C++ and C.
- __global__: This is a CUDA keyword that indicates the function is a kernel that runs on the GPU.
- void my_add(const float* x1, const float* x2, float* y): This is the kernel function definition. It takes three pointers to float arrays as arguments.
- int tid = blockDim.x * blockIdx.x + threadIdx.x;: This calculates the thread ID (tid) based on the block and thread indices. Each thread will have a unique tid.
- y[tid] = x1[tid] + x2[tid];: This performs the element-wise addition for the element corresponding to the thread ID.

Usage Example:

import cupy as cp

# Define the kernel
add_kernel = cp.RawKernel(r'''
    extern "C" __global__
    void my_add(const float* x1, const float* x2, float* y)
    {
        int tid = blockDim.x * blockIdx.x + threadIdx.x;
        y[tid] = x1[tid] + x2[tid];
    }''', 'my_add')

# Create input arrays
x1 = cp.array([1, 2, 3, 4, 5], dtype=cp.float32)
x2 = cp.array([10, 20, 30, 40, 50], dtype=cp.float32)
y = cp.empty_like(x1)

# Launch the kernel
threads_per_block = 32
blocks_per_grid = (x1.size + threads_per_block - 1) // threads_per_block
add_kernel((blocks_per_grid,), (threads_per_block,), (x1, x2, y))

# Print the result
print(y)  # Output: [11. 22. 33. 44. 55.]

Key Points

CuPy’s ElementwiseKernel allows custom element-wise GPU operations.

ReductionKernel is used for custom reduction operations on arrays.

RawKernel enables defining custom CUDA kernels using CUDA C/C++.

Elementwise and reduction kernels simplify GPU programming for specific tasks.