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Change test and examples from using camelCase to using snake_case as implementation changed

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Oleksandr Pavlyk
2025-07-21 12:50:28 -05:00
parent 81fff085b9
commit e589518376
9 changed files with 101 additions and 105 deletions
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57
python/examples/axes.py
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@@ -43,13 +43,13 @@ __global__ void sleep_kernel(double seconds) {
def simple(state: nvbench.State):
state.setMinSamples(1000)
state.set_min_samples(1000)
sleep_dur = 1e-3
krn = make_sleep_kernel()
launch_config = core.LaunchConfig(grid=1, block=1, shmem_size=0)
def launcher(launch: nvbench.Launch):
s = as_core_Stream(launch.getStream())
s = as_core_Stream(launch.get_stream())
core.launch(s, launch_config, krn, sleep_dur)
state.exec(launcher)
@@ -57,12 +57,13 @@ def simple(state: nvbench.State):
def single_float64_axis(state: nvbench.State):
# get axis value, or default
sleep_dur = state.getFloat64("Duration", 3.14e-4)
default_sleep_dur = 3.14e-4
sleep_dur = state.get_float64("Duration", default_sleep_dur)
krn = make_sleep_kernel()
launch_config = core.LaunchConfig(grid=1, block=1, shmem_size=0)
def launcher(launch: nvbench.Launch):
s = as_core_Stream(launch.getStream())
s = as_core_Stream(launch.get_stream())
core.launch(s, launch_config, krn, sleep_dur)
state.exec(launcher)
@@ -104,19 +105,19 @@ __global__ void copy_kernel(const T *in, U *out, ::cuda::std::size_t n)
def copy_sweep_grid_shape(state: nvbench.State):
block_size = state.getInt64("BlockSize")
num_blocks = state.getInt64("NumBlocks")
block_size = state.get_int64("BlockSize")
num_blocks = state.get_int64("NumBlocks")
# Number of int32 elements in 256MiB
nbytes = 256 * 1024 * 1024
num_values = nbytes // ctypes.sizeof(ctypes.c_int32(0))
state.addElementCount(num_values)
state.addGlobalMemoryReads(nbytes)
state.addGlobalMemoryWrites(nbytes)
state.add_element_count(num_values)
state.add_global_memory_reads(nbytes)
state.add_global_memory_writes(nbytes)
dev_id = state.getDevice()
alloc_s = as_core_Stream(state.getStream())
dev_id = state.get_device()
alloc_s = as_core_Stream(state.get_stream())
input_buf = core.DeviceMemoryResource(dev_id).allocate(nbytes, alloc_s)
output_buf = core.DeviceMemoryResource(dev_id).allocate(nbytes, alloc_s)
@@ -124,20 +125,20 @@ def copy_sweep_grid_shape(state: nvbench.State):
launch_config = core.LaunchConfig(grid=num_blocks, block=block_size, shmem_size=0)
def launcher(launch: nvbench.Launch):
s = as_core_Stream(launch.getStream())
s = as_core_Stream(launch.get_stream())
core.launch(s, launch_config, krn, input_buf, output_buf, num_values)
state.exec(launcher)
def copy_type_sweep(state: nvbench.State):
type_id = state.getInt64("TypeID")
type_id = state.get_int64("TypeID")
types_map = {
0: (ctypes.c_uint8, "::cuda::std::uint8_t"),
1: (ctypes.c_uint16, "::cuda::std::uint16_t"),
2: (ctypes.c_uint32, "::cuda::std::uint32_t"),
3: (ctypes.c_uint64, "::cuda::std::uint64_t"),
0: (ctypes.c_uint8, "cuda::std::uint8_t"),
1: (ctypes.c_uint16, "cuda::std::uint16_t"),
2: (ctypes.c_uint32, "cuda::std::uint32_t"),
3: (ctypes.c_uint64, "cuda::std::uint64_t"),
4: (ctypes.c_float, "float"),
5: (ctypes.c_double, "double"),
}
@@ -149,12 +150,12 @@ def copy_type_sweep(state: nvbench.State):
nbytes = 256 * 1024 * 1024
num_values = nbytes // ctypes.sizeof(value_ctype(0))
state.addElementCount(num_values)
state.addGlobalMemoryReads(nbytes)
state.addGlobalMemoryWrites(nbytes)
state.add_element_count(num_values)
state.add_global_memory_reads(nbytes)
state.add_global_memory_writes(nbytes)
dev_id = state.getDevice()
alloc_s = as_core_Stream(state.getStream())
dev_id = state.get_device()
alloc_s = as_core_Stream(state.get_stream())
input_buf = core.DeviceMemoryResource(dev_id).allocate(nbytes, alloc_s)
output_buf = core.DeviceMemoryResource(dev_id).allocate(nbytes, alloc_s)
@@ -162,7 +163,7 @@ def copy_type_sweep(state: nvbench.State):
launch_config = core.LaunchConfig(grid=256, block=256, shmem_size=0)
def launcher(launch: nvbench.Launch):
s = as_core_Stream(launch.getStream())
s = as_core_Stream(launch.get_stream())
core.launch(s, launch_config, krn, input_buf, output_buf, num_values)
state.exec(launcher)
@@ -175,13 +176,15 @@ if __name__ == "__main__":
# benchmark with no axes, that uses default value
nvbench.register(default_value)
# specify axis
nvbench.register(single_float64_axis).addFloat64Axis("Duration", [7e-5, 1e-4, 5e-4])
nvbench.register(single_float64_axis).add_float64_axis(
"Duration", [7e-5, 1e-4, 5e-4]
)
copy1_bench = nvbench.register(copy_sweep_grid_shape)
copy1_bench.addInt64Axis("BlockSize", [2**x for x in range(6, 10, 2)])
copy1_bench.addInt64Axis("NumBlocks", [2**x for x in range(6, 10, 2)])
copy1_bench.add_int64_axis("BlockSize", [2**x for x in range(6, 10, 2)])
copy1_bench.add_int64_axis("NumBlocks", [2**x for x in range(6, 10, 2)])
copy2_bench = nvbench.register(copy_type_sweep)
copy2_bench.addInt64Axis("TypeID", range(0, 6))
copy2_bench.add_int64_axis("TypeID", range(0, 6))
nvbench.run_all_benchmarks(sys.argv)