Skip to content
Snippets Groups Projects
Select Git revision
  • main default protected
1 result
Compare History
user avatar
[Utils][Container] Add copy and move constructors and assignment operators. (#11)
zhanghb97 authored
cfc5647d
Name Last commit Last update
benchmarks
docs
include
lib
tests
.clang-format
.gitignore
CMakeLists.txt
LICENSE
README.md
README.md

Buddy Benchmark

Buddy Benchmark is an extensible benchmark framework. We intend to provide a platform for performance comparison of various frameworks and optimizers. This project is based on Google Benchmark.

Clone the project:

$ git clone git@github.com:buddy-compiler/buddy-benchmark.git

Image Processing Benchmark

Currently, the image processing benchmark includes the following frameworks or optimizers:

NOTE: Please build OpenCV from source to achieve the best performance.

NOTE: Please make sure the buddy-opt tool of buddy-mlir project can work well.

Run the image processing benchmark:

$ cd buddy-benchmark
$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DIMAGE_PROCESSING_BENCHMARKS=ON \
    -DOpenCV_DIR=/path/to/opencv/build/ \
    -DBUDDY_OPT_BUILD_DIR=/path/to/buddy-mlir/build/ \
    -DBUDDY_OPT_STRIP_MINING=<strip mining size, default: 256> \
    -DBUDDY_OPT_ATTR=<ISA vector extension, default: avx512f>
$ ninja image-processing-benchmark
$ cd bin && ./image-processing-benchmark

Note : The convolution implementation in buddy mlir is not feature complete at the moment and it may produce output which differs to some extent from the frameworks used in comparison.

Deep Learning Benchmark

$ cd buddy-benchmark
$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DDEEP_LEARNING_BENCHMARKS=ON \
    -DOpenCV_DIR=/path/to/opencv/build/ \
    -DBUDDY_OPT_BUILD_DIR=/path/to/buddy-mlir/build/ \
    -DBUDDY_OPT_ATTR=<ISA vector extension, default: avx512f>
$ ninja

The deep learning benchmark includes the following e2e models and operations:

  • MobileNet

NOTE: We generated the model code with IREE and made appropriate modifications, and then compiled it with the MLIR tool chain.

Run the MobileNet benchmark:

$ cd <path to build>/bin && ./mobilenet-benchmark
  • DepthwiseConv2DNhwcHwc Operation

Run the DepthwiseConv2DNhwcHwc operation benchmark:

$ cd <path to build>/bin && ./depthwise-conv-2d-nhwc-hwc-benchmark

Testing

$ cd buddy-benchmark
$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DIMAGE_PROCESSING_BENCHMARKS=ON \
    -DDEEP_LEARNING_BENCHMARKS=ON \
    -DBUILD_TESTS=ON \
    -DOpenCV_DIR=/path/to/opencv/build/ \
    -DBUDDY_OPT_BUILD_DIR=/path/to/buddy-mlir/build/ \
    -DBUDDY_OPT_STRIP_MINING=<strip mining size, default: 256> \
    -DBUDDY_OPT_ATTR=<ISA vector extension, default: avx512f>
$ cmake --build . --
$ ninja test