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Improvements to readability of examples per PR review

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This commit is contained in:
Oleksandr Pavlyk
2025-07-31 16:20:52 -05:00
parent c91204f259
commit 453a1648aa
6 changed files with 30 additions and 30 deletions
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10
python/examples/auto_throughput.py
View File

@@ -21,11 +21,11 @@ import numpy as np
from numba import cuda
def as_cuda_Stream(cs: nvbench.CudaStream) -> cuda.cudadrv.driver.Stream:
def as_cuda_stream(cs: nvbench.CudaStream) -> cuda.cudadrv.driver.Stream:
return cuda.external_stream(cs.addressof())
def make_kernel(items_per_thread: int) -> cuda.dispatcher.CUDADispatcher:
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)
@@ -46,7 +46,7 @@ def throughput_bench(state: nvbench.State) -> None:
nbytes = 128 * 1024 * 1024
elements = nbytes // np.dtype(np.int32).itemsize
alloc_stream = as_cuda_Stream(state.get_stream())
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)
@@ -56,7 +56,7 @@ def throughput_bench(state: nvbench.State) -> None:
threads_per_block = 256
blocks_in_grid = (elements + threads_per_block - 1) // threads_per_block
krn = make_kernel(ipt)
krn = make_throughput_kernel(ipt)
# warm-up call ensures that kernel is loaded into context
# before blocking kernel is launched. Kernel loading may cause
@@ -66,7 +66,7 @@ def throughput_bench(state: nvbench.State) -> None:
)
def launcher(launch: nvbench.Launch):
exec_stream = as_cuda_Stream(launch.get_stream())
exec_stream = as_cuda_stream(launch.get_stream())
krn[blocks_in_grid, threads_per_block, exec_stream, 0](
stride, elements, inp_arr, out_arr
)

2
python/examples/axes.py
View File

@@ -193,7 +193,7 @@ if __name__ == "__main__":
nvbench.register(default_value)
# specify axis
nvbench.register(single_float64_axis).add_float64_axis(
"Duration", [7e-5, 1e-4, 5e-4]
"Duration (s)", [7e-5, 1e-4, 5e-4]
)
copy1_bench = nvbench.register(copy_sweep_grid_shape)

6
python/examples/cccl_parallel_segmented_reduce.py
View File

@@ -61,10 +61,6 @@ def segmented_reduce(state: nvbench.State):
dev_id = state.get_device()
cp_stream = as_cp_ExternalStream(state.get_stream(), dev_id)
with cp_stream:
rng = cp.random.default_rng()
mat = rng.integers(low=-31, high=32, dtype=np.int32, size=(n_rows, n_cols))
def add_op(a, b):
return a + b
@@ -84,6 +80,8 @@ def segmented_reduce(state: nvbench.State):
h_init = np.zeros(tuple(), dtype=np.int32)
with cp_stream:
rng = cp.random.default_rng()
mat = rng.integers(low=-31, high=32, dtype=np.int32, size=(n_rows, n_cols))
d_input = mat
d_output = cp.empty(n_rows, dtype=d_input.dtype)

10
python/examples/cupy_extract.py
View File

@@ -36,14 +36,16 @@ def cupy_extract_by_mask(state: nvbench.State):
state.collect_cupti_metrics()
state.add_element_count(n_rows * n_cols, "# Elements")
int32_dt = cp.dtype(cp.int32)
bool_dt = cp.dtype(cp.bool_)
state.add_global_memory_reads(
n_rows * n_cols * (cp.dtype(cp.int32).itemsize + cp.dtype("?").itemsize)
n_rows * n_cols * (int32_dt.itemsize + bool_dt.itemsize)
)
state.add_global_memory_writes(n_rows * n_cols * (cp.dtype(cp.int32).itemsize))
state.add_global_memory_writes(n_rows * n_cols * (int32_dt.itemsize))
with cp_s:
X = cp.full((n_cols, n_rows), fill_value=3, dtype=cp.int32)
mask = cp.ones((n_cols, n_rows), dtype="?")
X = cp.full((n_cols, n_rows), fill_value=3, dtype=int32_dt)
mask = cp.ones((n_cols, n_rows), dtype=bool_dt)
_ = X[mask]
def launcher(launch: nvbench.Launch):

10
python/examples/throughput.py
View File

@@ -21,11 +21,11 @@ import numpy as np
from numba import cuda
def as_cuda_Stream(cs: nvbench.CudaStream) -> cuda.cudadrv.driver.Stream:
def as_cuda_stream(cs: nvbench.CudaStream) -> cuda.cudadrv.driver.Stream:
return cuda.external_stream(cs.addressof())
def make_kernel(items_per_thread: int) -> cuda.dispatcher.CUDADispatcher:
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)
@@ -46,7 +46,7 @@ def throughput_bench(state: nvbench.State) -> None:
nbytes = 128 * 1024 * 1024
elements = nbytes // np.dtype(np.int32).itemsize
alloc_stream = as_cuda_Stream(state.get_stream())
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)
@@ -57,7 +57,7 @@ def throughput_bench(state: nvbench.State) -> None:
threads_per_block = 256
blocks_in_grid = (elements + threads_per_block - 1) // threads_per_block
krn = make_kernel(ipt)
krn = make_throughput_kernel(ipt)
# warm-up call ensures that kernel is loaded into context
# before blocking kernel is launched. Kernel loading may
@@ -67,7 +67,7 @@ def throughput_bench(state: nvbench.State) -> None:
)
def launcher(launch: nvbench.Launch):
exec_stream = as_cuda_Stream(launch.get_stream())
exec_stream = as_cuda_stream(launch.get_stream())
krn[blocks_in_grid, threads_per_block, exec_stream, 0](
stride, elements, inp_arr, out_arr
)

22
python/test/run_1.py
View File

@@ -14,12 +14,15 @@ def kernel(a, b, c):
c[tid] = a[tid] + b[tid]
def get_numba_stream(launch):
def get_numba_stream(launch: nvbench.Launch):
return cuda.external_stream(launch.get_stream().addressof())
def add_two(state):
# state.skip("Skipping this benchmark for no reason")
def skipit(state: nvbench.State) -> None:
state.skip("Skipping this benchmark for no reason")
def add_two(state: nvbench.State):
N = state.get_int64("elements")
a = cuda.to_device(np.random.random(N))
c = cuda.device_array_like(a)
@@ -44,7 +47,7 @@ def add_two(state):
state.exec(kernel_launcher, batched=True, sync=True)
def add_float(state):
def add_float(state: nvbench.State):
N = state.get_int64("elements")
v = state.get_float64("v")
name = state.get_string("name")
@@ -75,7 +78,7 @@ def add_float(state):
state.exec(kernel_launcher, batched=True, sync=True)
def add_three(state):
def add_three(state: nvbench.State):
N = state.get_int64("elements")
a = cuda.to_device(np.random.random(N).astype(np.float32))
b = cuda.to_device(np.random.random(N).astype(np.float32))
@@ -105,13 +108,10 @@ def register_benchmarks():
nvbench.register(add_float)
.add_float64_axis("v", [0.1, 0.3])
.add_string_axis("name", ["Anne", "Lynda"])
.add_int64_axis("elements", [2**pow2 for pow2 in range(20, 23)])
)
(
nvbench.register(add_three).add_int64_axis(
"elements", [2**pow2 for pow2 in range(20, 22)]
)
.add_int64_power_of_two_axis("elements", range(20, 23))
)
(nvbench.register(add_three).add_int64_power_of_two_axis("elements", range(20, 22)))
nvbench.register(skipit)
if __name__ == "__main__":