From 2507bc226352cd2e628ed705eed8f47074fa357b Mon Sep 17 00:00:00 2001
From: Oleksandr Pavlyk <21087696+oleksandr-pavlyk@users.noreply.github.com>
Date: Tue, 1 Jul 2025 14:59:33 -0500
Subject: [PATCH] Add Python example based on C++ example/auto_throughput.cpp

---
 python/examples/auto_throughput.py | 75 ++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 python/examples/auto_throughput.py

diff --git a/python/examples/auto_throughput.py b/python/examples/auto_throughput.py
new file mode 100644
index 0000000..4f79217
--- /dev/null
+++ b/python/examples/auto_throughput.py
@@ -0,0 +1,75 @@
+# Copyright 2025 NVIDIA Corporation
+#
+#  Licensed under the Apache License, Version 2.0 with the LLVM exception
+#  (the "License"); you may not use this file except in compliance with
+#  the License.
+#
+#  You may obtain a copy of the License at
+#
+#      http://llvm.org/foundation/relicensing/LICENSE.txt
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import sys
+
+import cuda.nvbench as nvbench
+import numpy as np
+from numba import cuda
+
+
+def make_kernel(items_per_thread: int):
+    @cuda.jit
+    def kernel(stride: np.uintp, elements: np.uintp, in_arr, out_arr):
+        tid = cuda.grid(1)
+        step = cuda.gridDim.x * cuda.blockDim.x
+        for i in range(stride * tid, stride * elements, stride * step):
+            for j in range(items_per_thread):
+                read_id = (items_per_thread * i + j) % elements
+                write_id = tid + j * elements
+                out_arr[write_id] = in_arr[read_id]
+
+    return kernel
+
+
+def throughput_bench(state: nvbench.State):
+    stride = state.getInt64("Stride")
+    ipt = state.getInt64("ItemsPerThread")
+
+    nbytes = 128 * 1024 * 1024
+    elements = nbytes // np.dtype(np.int32).itemsize
+
+    alloc_stream = cuda.external_stream(state.getStream().addressof())
+    inp_arr = cuda.device_array(elements, dtype=np.int32, stream=alloc_stream)
+    out_arr = cuda.device_array(elements * ipt, dtype=np.int32, stream=alloc_stream)
+
+    state.addElementCount(elements, "Elements")
+    state.collectCUPTIMetrics()
+
+    threads_per_block = 256
+    blocks_in_grid = (elements + threads_per_block - 1) // threads_per_block
+
+    krn = make_kernel(ipt)
+
+    def launcher(launch: nvbench.Launch):
+        exec_stream = cuda.external_stream(launch.getStream().addressof())
+        krn[blocks_in_grid, threads_per_block, exec_stream, 0](
+            stride, elements, inp_arr, out_arr
+        )
+
+    state.exec(launcher)
+
+
+(
+    nvbench.register(throughput_bench)
+    .addInt64Axis("Stride", [1, 4])
+    .addInt64Axis("ItemsPerThread", [1, 2, 3, 4])
+)
+
+
+if __name__ == "__main__":
+    print(nvbench.__version__)
+    nvbench.run_all_benchmarks(sys.argv)