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 [email protected]:buddy-compiler/buddy-benchmark.git

Choose and Build Dependencies

Choose Submodules

$ git submodule update --init

Deep Learning Benchmark

Please check the deep learning benchmark document at this link.

Image Processing Benchmark

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

• OpenCV (link)

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

Build OpenCV:

$ cd buddy-benchmark/thirdparty/opencv
$ mkdir build && cd build
$ cmake -G Ninja .. -DCMAKE_BUILD_TYPE=Release
$ ninja

• Eigen (link)

• Buddy MLIR (link)

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

Run the image processing benchmark:

CMake Options	Default Value
-DBUDDY_OPT_STRIP_MINING	256
-DMLIR_LINALG_TILE	2
-DBUDDY_OPT_ATTR	avx512f
-DBUDDY_OPT_TRIPLE	x86_64-unknown-linux-gnu

Note:

1. Please replace the /PATH/TO/* with your local path.

2. For running executable :

i. Please replace <image path> with path of the image which is to be used for benchmarking.

ii. Please replace <kernel name> with name of the kernel which is to be used for benchmarking as specifed in include/ImageProcessing/Kernels.h.

ii. Please replace <kernelmorph name> with name of the unsigned int kernel which is to be used for benchmarking as specifed in include/ImageProcessing/Kernels.h.

iii. Please replace <Boundary Option> with CONSTANT_PADDING or REPLICATE_PADDING.

Ex. ./image-processing-benchmark ../../benchmarks/ImageProcessing/Images/YuTu.png random3x3KernelAlign random3x3KernelAlignInt CONSTANT_PADDING

$ cd buddy-benchmark
$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DIMAGE_PROCESSING_BENCHMARKS=ON \
    -DOpenCV_DIR=$PWD/../thirdparty/opencv/build/ \
    -DEIGEN_DIR=$PWD/../thirdparty/eigen/ \
    -DBUDDY_MLIR_BUILD_DIR=/PATH/TO/BUDDY-MLIR/BUILD/
$ ninja image-processing-benchmark
$ cd bin && ./image-processing-benchmark <image path> <kernel name> <kernelmorph name> <Boundary Option>

Audio Processing Benchmark

Please check the audio processing benchmark document here.

Vectorization Benchmark

Some of the benchmarks are ported from gcc-loops(link) in LLVM test suit and linpackc(link)

Note: Please replace the /PATH/TO/* with your local path and the XXX with specific target name (ex: gccloops,linpackc,matrix).

$ cd buddy-benchmark
$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DVECTORIZATION_BENCHMARKS=ON \
    -DBUDDY_MLIR_BUILD_DIR=/PATH/TO/BUDDY-MLIR/BUILD/
$ ninja vectorization-XXX-benchmark
$ cd bin
$ ./vectorization-XXX-benchmark

Gemmini Benchmark

Currently, we use the Spike simulator to run the Gemmini cases. The cycle-accurate benchmark cases are working in the progress. Before building the benchmark target, please see the following table and ensure you use the correct configuration.

Cases	Hardware Configuration
Gemmini-ResNet-101	defaultFpConfig (link)

We assume you have already built all the components in the Gemmini README file. Now, let's build and run the cases.

$ source /path/to/chipyard/env.sh
$ cd buddy-benchmark
$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DBUDDY_MLIR_BUILD_DIR=/PATH/TO/BUDDY-MLIR/BUILD/ \
    -DGEMMINI_BENCHMARKS=ON
$ ninja
$ cd bin
$ spike --extension=gemmini pk Gemmini-ResNet-101

Operation Optimization Benchmark

Build and run MLIR operation optimization benchmark cases.

$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DOP_OPTIMIZATION_BENCHMARKS=ON \
    -DBUDDY_MLIR_BUILD_DIR=/PATH/TO/BUDDY-MLIR/BUILD/
$ ninja <your target operation benchmark>

// Operation benchamrk supported include:
//   - conv2d-nchw-fchw-benchmark
//   - matmul-benchmark

Run TVM operation optimization benchmark cases.

• Install TVM (steps).
• Enter to your TVM (virtual) environment.
• Configure TVM path and Python path.
• Navigate to your target operation directory (e.g. buddy-benchmark/benchmarks/OpOptimization/MatMul/TVM).
• (Optional) Configure the main file to specify the target or size of the benchmark.
• Run the main python file.

(tvm)$ export TVM_HOME=/path/to/tvm
(tvm)$ export PYTHONPATH=$TVM_HOME/python:${PYTHONPATH}
(tvm)$ cd benchmarks/OpOptimization/<target operation>/TVM
(tvm)$ python main.py