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Running CUB Benchmarks

This file contains instrutions on how to run all CUB benchmarks using CUB tuning infrastructure.

pip3 install --user fpzip pandas scipy
git clone https://github.com/NVIDIA/cccl.git
cmake -B build -DCCCL_ENABLE_THRUST=OFF\
         -DCCCL_ENABLE_LIBCUDACXX=OFF\
         -DCCCL_ENABLE_CUB=ON\
         -DCUB_ENABLE_DIALECT_CPP11=OFF\
         -DCUB_ENABLE_DIALECT_CPP14=OFF\
         -DCUB_ENABLE_DIALECT_CPP17=ON\
         -DCUB_ENABLE_DIALECT_CPP20=OFF\
         -DCUB_ENABLE_RDC_TESTS=OFF\
         -DCUB_ENABLE_BENCHMARKS=YES\
         -DCUB_ENABLE_TUNING=YES\
         -DCMAKE_BUILD_TYPE=Release\
         -DCMAKE_CUDA_ARCHITECTURES="89;90"
cd build
../cub/benchmarks/scripts/run.py

Expected output for the command above is:

../cub/benchmarks/scripts/run.py
&&&& RUNNING bench
ctk:  12.2.140
cub:  812ba98d1
&&&& PERF cub_bench_adjacent_difference_subtract_left_base_T_ct__I32___OffsetT_ct__I32___Elements_io__pow2__16 4.095999884157209e-06 -sec
&&&& PERF cub_bench_adjacent_difference_subtract_left_base_T_ct__I32___OffsetT_ct__I32___Elements_io__pow2__20 1.2288000107218977e-05 -sec
&&&& PERF cub_bench_adjacent_difference_subtract_left_base_T_ct__I32___OffsetT_ct__I32___Elements_io__pow2__24 0.00016998399223666638 -sec
&&&& PERF cub_bench_adjacent_difference_subtract_left_base_T_ct__I32___OffsetT_ct__I32___Elements_io__pow2__28 0.002673664130270481 -sec
...

It's also possible to benchmark a subset of algorithms and workloads:

../cub/benchmarks/scripts/run.py -R '.*scan.exclusive.sum.*' -a 'Elements{io}[pow2]=[24,28]' -a 'T{ct}=I32'
&&&& RUNNING bench
ctk:  12.2.140
cub:  812ba98d1
&&&& PERF cub_bench_scan_exclusive_sum_base_T_ct__I32___OffsetT_ct__I32___Elements_io__pow2__24 0.00016899200272746384 -sec
&&&& PERF cub_bench_scan_exclusive_sum_base_T_ct__I32___OffsetT_ct__I32___Elements_io__pow2__28 0.002696000039577484 -sec
&&&& PASSED bench