diff --git a/python/examples/study/with_blocking_krn.py b/python/examples/study/with_blocking_krn.py
new file mode 100644
index 0000000..ff02bd3
--- /dev/null
+++ b/python/examples/study/with_blocking_krn.py
@@ -0,0 +1,76 @@
+# 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.bench as bench
+import numpy as np
+from numba import cuda
+
+
+def as_cuda_stream(cs: bench.CudaStream) -> cuda.cudadrv.driver.Stream:
+    return cuda.external_stream(cs.addressof())
+
+
+def make_throughput_kernel(items_per_thread: int) -> cuda.dispatcher.CUDADispatcher:
+    @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: bench.State) -> None:
+    stride = state.get_int64("Stride")
+    ipt = state.get_int64("ItemsPerThread")
+
+    nbytes = 128 * 1024 * 1024
+    elements = nbytes // np.dtype(np.int32).itemsize
+
+    alloc_stream = as_cuda_stream(state.get_stream())
+    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.add_element_count(elements, column_name="Elements")
+    state.add_global_memory_reads(inp_arr.nbytes, column_name="Datasize")
+    state.add_global_memory_writes(inp_arr.nbytes)
+
+    threads_per_block = 256
+    blocks_in_grid = (elements + threads_per_block - 1) // threads_per_block
+
+    krn = make_throughput_kernel(ipt)
+
+    def launcher(launch: bench.Launch):
+        exec_stream = as_cuda_stream(launch.get_stream())
+        krn[blocks_in_grid, threads_per_block, exec_stream, 0](
+            stride, elements, inp_arr, out_arr
+        )
+
+    state.exec(launcher)
+
+
+if __name__ == "__main__":
+    b = bench.register(throughput_bench)
+    b.add_int64_axis("Stride", [1, 2, 4])
+    b.add_int64_axis("ItemsPerThread", [1, 2, 3, 4])
+
+    bench.run_all_benchmarks(sys.argv)
diff --git a/python/examples/study/without_blocking_krn.py b/python/examples/study/without_blocking_krn.py
new file mode 100644
index 0000000..1a22256
--- /dev/null
+++ b/python/examples/study/without_blocking_krn.py
@@ -0,0 +1,76 @@
+# 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.bench as bench
+import numpy as np
+from numba import cuda
+
+
+def as_cuda_stream(cs: bench.CudaStream) -> cuda.cudadrv.driver.Stream:
+    return cuda.external_stream(cs.addressof())
+
+
+def make_throughput_kernel(items_per_thread: int) -> cuda.dispatcher.CUDADispatcher:
+    @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: bench.State) -> None:
+    stride = state.get_int64("Stride")
+    ipt = state.get_int64("ItemsPerThread")
+
+    nbytes = 128 * 1024 * 1024
+    elements = nbytes // np.dtype(np.int32).itemsize
+
+    alloc_stream = as_cuda_stream(state.get_stream())
+    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.add_element_count(elements, column_name="Elements")
+    state.add_global_memory_reads(inp_arr.nbytes, column_name="Datasize")
+    state.add_global_memory_writes(inp_arr.nbytes)
+
+    threads_per_block = 256
+    blocks_in_grid = (elements + threads_per_block - 1) // threads_per_block
+
+    krn = make_throughput_kernel(ipt)
+
+    def launcher(launch: bench.Launch):
+        exec_stream = as_cuda_stream(launch.get_stream())
+        krn[blocks_in_grid, threads_per_block, exec_stream, 0](
+            stride, elements, inp_arr, out_arr
+        )
+
+    state.exec(launcher, sync=True)
+
+
+if __name__ == "__main__":
+    b = bench.register(throughput_bench)
+    b.add_int64_axis("Stride", [1, 2, 4])
+    b.add_int64_axis("ItemsPerThread", [1, 2, 3, 4])
+
+    bench.run_all_benchmarks(sys.argv)