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104 lines
3.1 KiB
Python
104 lines
3.1 KiB
Python
# Copyright 2025 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.cccl.cooperative.experimental as coop
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import cuda.nvbench as nvbench
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import numba
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import numpy as np
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from numba import cuda
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from pynvjitlink import patch
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class BitsetRing:
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"""
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Addition operation over ring fixed width unsigned integers
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with ring_plus = bitwise_or and ring_mul = bitwise_and,
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ring_zero = 0, ring_one = -1
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"""
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def __init__(self):
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self.dt = np.uint64
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self.zero = self.dt(0)
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self.one = np.bitwise_invert(self.zero)
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@staticmethod
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def add(op1, op2):
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return op1 | op2
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@staticmethod
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def mul(op1, op2):
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return op1 & op2
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def as_cuda_Stream(cs: nvbench.CudaStream) -> cuda.cudadrv.driver.Stream:
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return cuda.external_stream(cs.addressof())
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def multi_block_bench(state: nvbench.State):
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threads_per_block = state.get_int64("ThreadsPerBlock")
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num_blocks = state.get_int64("NumBlocks")
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total_elements = threads_per_block * num_blocks
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if total_elements > 2**26:
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state.skip(reason="Memory footprint over threshold")
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return
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ring = BitsetRing()
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block_reduce = coop.block.reduce(numba.uint64, threads_per_block, BitsetRing.add)
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@cuda.jit(link=block_reduce.files)
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def kernel(inp_arr, out_arr):
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# Each thread contributes one element
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block_idx = cuda.blockIdx.x
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thread_idx = cuda.threadIdx.x
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global_idx = block_idx * threads_per_block + thread_idx
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block_output = block_reduce(inp_arr[global_idx])
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# Only thread 0 of each block writes the result
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if thread_idx == 0:
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out_arr[block_idx] = block_output
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h_inp = np.arange(1, total_elements + 1, dtype=ring.dt)
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d_inp = cuda.to_device(h_inp)
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d_out = cuda.device_array(num_blocks, dtype=ring.dt)
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cuda_s = as_cuda_Stream(state.get_stream())
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# warmup
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kernel[num_blocks, threads_per_block, cuda_s, 0](d_inp, d_out)
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state.add_element_count(total_elements)
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state.add_global_memory_reads(total_elements * h_inp.itemsize)
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state.add_global_memory_writes(num_blocks * h_inp.itemsize)
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def launcher(launch: nvbench.Launch):
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cuda_s = as_cuda_Stream(launch.get_stream())
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kernel[num_blocks, threads_per_block, cuda_s, 0](d_inp, d_out)
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state.exec(launcher)
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if __name__ == "__main__":
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patch.patch_numba_linker(lto=True)
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b = nvbench.register(multi_block_bench)
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b.add_int64_axis("ThreadsPerBlock", [64, 128, 192, 256])
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b.add_int64_power_of_two_axis("NumBlocks", [10, 11, 12, 14, 16])
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nvbench.run_all_benchmarks(sys.argv)
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