cpu_only -> cpu_activity

Change example to illustrate timing CPU work.

First example does only CPU work (sleeps), use CPU-only timer.
Second examples does both CPU and GPU work (sleeps in either case).
Use cold-run timer with/without sync tag to measure both CPU and GPU times.

python/examples/cpu_activity.py

@@ -0,0 +1,81 @@
+import sys
+import time
+
+import cuda.cccl.headers as headers
+import cuda.core.experimental as core
+import cuda.nvbench as nvbench
+
+host_sleep_duration = 0.1
+
+
+def cpu_only_sleep_bench(state: nvbench.State) -> None:
+    def launcher(launch: nvbench.Launch):
+        time.sleep(host_sleep_duration)
+
+    state.exec(launcher)
+
+
+def as_core_Stream(cs: nvbench.CudaStream) -> core.Stream:
+    return core.Stream.from_handle(cs.addressof())
+
+
+def make_sleep_kernel():
+    """JITs sleep_kernel(seconds)"""
+    src = r"""
+#include <cuda/std/cstdint>
+#include <cuda/std/chrono>
+
+// Each launched thread just sleeps for `seconds`.
+__global__ void sleep_kernel(double seconds) {
+  namespace chrono = ::cuda::std::chrono;
+  using hr_clock = chrono::high_resolution_clock;
+
+  auto duration = static_cast<cuda::std::int64_t>(seconds * 1e9);
+  const auto ns = chrono::nanoseconds(duration);
+
+  const auto start = hr_clock::now();
+  const auto finish = start + ns;
+
+  auto now = hr_clock::now();
+  while (now < finish)
+  {
+    now = hr_clock::now();
+  }
+}
+"""
+    incl = headers.get_include_paths()
+    opts = core.ProgramOptions(include_path=str(incl.libcudacxx))
+    prog = core.Program(src, code_type="c++", options=opts)
+    mod = prog.compile("cubin", name_expressions=("sleep_kernel",))
+    return mod.get_kernel("sleep_kernel")
+
+
+def mixed_sleep_bench(state: nvbench.State) -> None:
+    sync = state.get_string("Sync")
+    sync_flag = sync == "Do sync"
+
+    gpu_sleep_dur = 225e-3
+    krn = make_sleep_kernel()
+    launch_config = core.LaunchConfig(grid=1, block=1, shmem_size=0)
+
+    def launcher(launch: nvbench.Launch):
+        # host overhead
+        time.sleep(host_sleep_duration)
+        # GPU computation
+        s = as_core_Stream(launch.get_stream())
+        core.launch(s, launch_config, krn, gpu_sleep_dur)
+
+    state.exec(launcher, sync=sync_flag)
+
+
+if __name__ == "__main__":
+    # time function only doing work (sleeping) on the host
+    # using CPU timer only
+    b = nvbench.register(cpu_only_sleep_bench)
+    b.set_is_cpu_only(True)
+
+    # time the function that does work on both GPU and CPU
+    b2 = nvbench.register(mixed_sleep_bench)
+    b2.add_string_axis("Sync", ["Do not sync", "Do sync"])
+
+    nvbench.run_all_benchmarks(sys.argv)

python/examples/cpu_only.py

@@ -1,34 +0,0 @@
-import sys
-import time
-
-import cuda.nvbench as nvbench
-
-
-def sleep_bench(state: nvbench.State) -> None:
-    def launcher(launch: nvbench.Launch):
-        time.sleep(1)
-
-    state.exec(launcher)
-
-
-def sleep_bench_sync(state: nvbench.State) -> None:
-    sync = state.get_string("Sync")
-    sync_flag = sync == "Do sync"
-
-    def launcher(launch: nvbench.Launch):
-        time.sleep(1)
-
-    state.exec(launcher, sync=sync_flag)
-
-
-if __name__ == "__main__":
-    # time function sleeping on the host
-    # using CPU timer only
-    b = nvbench.register(sleep_bench)
-    b.set_is_cpu_only(True)
-
-    # time the same function using both CPU/GPU timers
-    b2 = nvbench.register(sleep_bench_sync)
-    b2.add_string_axis("Sync", ["Do not sync", "Do sync"])
-
-    nvbench.run_all_benchmarks(sys.argv)