add Cupy example for PythonKernel

Author: benvanwerkhoven

examples/cuda/python_kernel_cupy.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+"""
+    This is the vector_add example modified to show
+    how to use PythonKernel with the CuPy backend
+"""
+
+import cupy as cp
+import numpy as np
+from kernel_tuner.kernelbuilder import PythonKernel
+
+def kernelbuilder_example():
+
+    # To make this example self-contained we include the kernel as a string
+    # here, but you can also just point to a file with the kernel code
+    kernel_string = """
+    __global__ void vector_add(float *c, float *a, float *b, int n) {
+        int i = blockIdx.x * block_size_x + threadIdx.x;
+        if (i<n) {
+            c[i] = a[i] + b[i];
+        }
+    }
+    """
+
+    # Setup the arguments for our vector add kernel
+    size = 100000
+    a = cp.random.randn(size).astype(np.float32)
+    b = cp.random.randn(size).astype(np.float32)
+    c = cp.zeros_like(b)
+    n = np.int32(size)
+
+    # Note that the type and order should match our GPU code
+    # Because the arguments are all CuPy arrays, our PythonKernel does not need to
+    # worry about moving data between host and device
+    args = [c, a, b, n]
+
+    # We can instantiate a specific kernel configurations
+    params = {"block_size_x": 128}
+
+    # Here we construct a Python object that represents the kernel
+    # we can use it to conveniently use the GPU kernel in Python
+    # applications that want to frequently call the GPU kernel
+    vector_add = PythonKernel("vector_add", kernel_string, size, args, params, lang="cupy")
+
+    # We can use the PythonKernel instance as a regular Python function
+    vector_add(c, a, b, n)
+
+    # Compare the result in c with a+b computed in Python
+    assert np.allclose(c, a+b)
+
+
+if __name__ == "__main__":
+    kernelbuilder_example()