mirror of
https://github.com/NVIDIA/nvbench.git
synced 2026-05-13 17:55:39 +00:00
e07f87910a
cuda.bench.register(fn) continues returning Benchmark, and supports
legacy use.
New signature added:
cuda.bench.register():
Returns a decorator
```
@bench.register()
@bench.axis.float64("Duration (s)", [7e-5, 1e-4, 5e-4])
@bench.option.min_samples(120)
def single_float64_axis(state: bench.State):
...
```
76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
# Copyright 2025-2026 NVIDIA Corporation
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#
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# Licensed under the Apache License, Version 2.0 with the LLVM exception
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# (the "License"); you may not use this file except in compliance with
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# the License.
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#
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# You may obtain a copy of the License at
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#
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# http://llvm.org/foundation/relicensing/LICENSE.txt
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import cuda.bench as bench
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import numpy as np
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from numba import cuda
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def as_cuda_stream(cs: bench.CudaStream) -> cuda.cudadrv.driver.Stream:
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return cuda.external_stream(cs.addressof())
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def make_throughput_kernel(items_per_thread: int) -> cuda.dispatcher.CUDADispatcher:
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@cuda.jit
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def kernel(stride: np.uintp, elements: np.uintp, in_arr, out_arr):
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tid = cuda.grid(1)
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step = cuda.gridDim.x * cuda.blockDim.x
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for i in range(stride * tid, stride * elements, stride * step):
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for j in range(items_per_thread):
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read_id = (items_per_thread * i + j) % elements
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write_id = tid + j * elements
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out_arr[write_id] = in_arr[read_id]
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return kernel
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@bench.register()
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@bench.axis.int64("Stride", [1, 2, 4])
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@bench.axis.int64("ItemsPerThread", [1, 2, 3, 4])
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def throughput_bench(state: bench.State) -> None:
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stride = state.get_int64("Stride")
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ipt = state.get_int64("ItemsPerThread")
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nbytes = 128 * 1024 * 1024
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elements = nbytes // np.dtype(np.int32).itemsize
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alloc_stream = as_cuda_stream(state.get_stream())
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inp_arr = cuda.device_array(elements, dtype=np.int32, stream=alloc_stream)
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out_arr = cuda.device_array(elements * ipt, dtype=np.int32, stream=alloc_stream)
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state.add_element_count(elements, column_name="Elements")
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state.add_global_memory_reads(inp_arr.nbytes, column_name="Datasize")
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state.add_global_memory_writes(inp_arr.nbytes)
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threads_per_block = 256
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blocks_in_grid = (elements + threads_per_block - 1) // threads_per_block
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krn = make_throughput_kernel(ipt)
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def launcher(launch: bench.Launch):
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exec_stream = as_cuda_stream(launch.get_stream())
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krn[blocks_in_grid, threads_per_block, exec_stream, 0](
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stride, elements, inp_arr, out_arr
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)
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state.exec(launcher)
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if __name__ == "__main__":
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bench.run_all_benchmarks(sys.argv)
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