hkeee21 / PCEngine-AE

PCEngine-AE

This is the repository of PCEngine code for artifacts evaluation purpose. PCEngine is an optimized engine for voxel-based point cloud neural networks. Based on a noval coded-CSR format, indicator-assisted segmented GEMM (GEneral Matrix Multiplication) fusion and heuristics adaptive dataflow, PCEngine outperforms previous works in sparse convolution inference.

[Notice] This repository will not be updated in the future. Please refer to the official PCEngine repository for the latest version.

Requirements

    CUDA 11.1+
    PyTorch 1.10.0+
    TorchSparse 2.0.0
    SpConv 2.2.3+

Note the artifacts have been tested under following environments

    CUDA 11.1/11.6
    PyTorch 1.10.0/1.12.0
    TorchSparse 2.0.0
    SpConv 2.2.3/2.2.6

on an Nvidia RTX 2080 GPU or an Nvidia RTX 3090 GPU.

Setup

In order to use ncu_report package, please first config your $CUDA_HOME environment. It probably locates at /usr/local/cuda or something like that. Then config the ncu_report environment: export PYTHONPATH="${CUDA_HOME}/nsight-compute-xxxx.x.x/extras/python" If the path does not exist, try to use a higher version CUDA.

• Install PCEngine, TorchSparse and SpConv

      bash setup.sh
  

• (Optional) Install step by step

  • Install PCEngine

      cd lib/PCEngine
      python setup.py install
  

  • TorchSparse can be installed through the official repository as follows

      cd lib/TorchSparse
      git clone https://github.com/mit-han-lab/torchsparse.git
      cd torchsparse
      python setup.py install
  

  • SpConv can be installed through pip. Please refer to the official repository.

• Download datasets

  Please download AE-datasets and unzip the AE-datasets.zip file into a PCEngine-AE subdirectory as follows

  |---- PCEngine-AE directory
          |---- AE-datasets
          |       |---- ModelNet40
          |       |---- S3DIS
          |       |---- KITTI
          |
          |---- evaluation
          |---- correctness-check.py
          |---- lib
          |---- setup.sh
  

Evaluation

• To verify functionality

  Run the following command to compare the sparse convolution output with SpConv.

    python correctness.py
  

• To run artifacts evaluation

  Evaluate the end-to-end performance and generate the results into a .csv file (Fig. 9(a))

    cd evaluation
    python Fig9a-end-to-end.py
  

  Evaluate the sparse convolution performance and generate the results into a .csv file (Fig. 9(b))

    cd evaluation
    python Fig9b-kernel.py
  

  Compare gather and scatter performance to TorchSparse and generate the results into a .csv file (Fig. 10)

  • Add "--fast" to reduce the evaluation time.

    cd evaluation
    python Fig10-gather-scatter.py
  

  Conduct the ablation study on coded-CSR format and generate the results into a .csv file (Fig. 11)

  • Note that Fig11-coded-CSR.py and Fig10-gather-scatter.py can not be run simultaneously, as the two scripts reuse the same file to store intermediate results.
  • Add "--fast" to reduce the evaluation time.

    cd evaluation
    python Fig11-coded-CSR.py
  

  Conduct the ablation study on GEMM scheme and generate the results into a .csv file (Fig. 12)

    cd evaluation
    python Fig12-GEMM.py
  

  Conduct the ablation study on heuristics adaptive dataflow and generate the results into a .csv file (Fig. 13)

    cd evaluation
    python Fig13-heuristics.py