Add example that profiles parallel sum

examples/demo_flops.py

@@ -0,0 +1,66 @@
+import pyopencl as cl
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+src = """
+    __kernel void sum(__global T *x, __global T *y, __global T *z) {
+        const int i = get_global_id(0);
+
+        z[i] = x[i] + y[i];
+    }
+"""
+
+
+# allocates buffers of increasing size, for each run do a parallel sum interpreting
+# the buffer as an array of i8, i16, ...
+# profile the kernels to find the throughput in GFLOPS, useful to estimate the raw computational speed of the hardware
+if __name__ == '__main__':
+    types = [
+        ('i8' , 'char'  , 1),
+        ('i16', 'short' , 2),
+        ('i32', 'int'   , 4),
+        ('i64', 'long'  , 8),
+        # ('f16', 'half'  , 2),
+        ('f32', 'float' , 4),
+        ('f64', 'double', 8)
+    ]
+
+
+    ctx   = cl.create_some_context()
+    queue = cl.CommandQueue(ctx, properties = cl.command_queue_properties.PROFILING_ENABLE)
+
+
+    buffer_size = [2 ** i for i in range(10, 31)]
+    data = np.zeros((len(buffer_size), len(types)))
+
+    for row, nbytes in enumerate(buffer_size):
+        x = cl.Buffer(ctx, cl.mem_flags.READ_ONLY,  nbytes)
+        y = cl.Buffer(ctx, cl.mem_flags.READ_ONLY,  nbytes)
+        z = cl.Buffer(ctx, cl.mem_flags.WRITE_ONLY, nbytes)
+
+        for col, (label, literal, sizeof) in enumerate(types):
+            sums    = nbytes // sizeof
+            header  = f'#define T {literal}\n'
+            kernel  = cl.Program(ctx, header + src).build().sum
+
+            event   = kernel(queue, (sums,), None, x, y, z)
+            event.wait()
+
+            FLOPS = 1e9 * sums / (event.profile.end - event.profile.start)
+            GFLOPS = FLOPS / 1e6
+
+            data[row, col] = GFLOPS
+
+        x.release()
+        y.release()
+        z.release()
+
+    for col, (_, label, _) in enumerate(types):
+        plt.semilogx(buffer_size, data[:, col], label = label)
+
+    plt.title(f'{ctx.devices[0].name}')
+    plt.legend()
+    plt.xlabel('sizeof(vector)')
+    plt.ylabel('GFLOPS')
+    plt.show()