# NeuralPointProcess **Repository Path**: z2007c/NeuralPointProcess ## Basic Information - **Project Name**: NeuralPointProcess - **Description**: No description available - **Primary Language**: C++ - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2019-08-26 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # NeuralPointProcess #### Prerequisites Tested under Ubuntu 14.04 ##### Download and install cuda from https://developer.nvidia.com/cuda-toolkit wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_8.0.44-1_amd64.deb sudo dpkg -i cuda-repo-ubuntu1404_8.0.44-1_amd64.deb sudo apt-get update sudo apt-get install cuda in .bashrc, add the following path (suppose you installed to the default path) export CUDA_HOME=/usr/local/cuda export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH ##### Download and install intel mkl in .bashrc, add the following path source {path_to_your_intel_root/name_of_parallel_tool_box}/bin/psxevars.sh export MKL_ROOT={path_to_your_intel_root}/mkl ##### Install cppformat (now called fmtlib) check https://github.com/fmtlib/fmt for help ##### Build static graphnn v1.11 library navigate to code/graphnn-1.11 modify configurations in make_common file make #### Build main nn code navigate to code/nn make #### run test navigate to code/nn ./synthetic_run.sh #### reproduce the results reported in paper navigate to code/nn modify the scripts under code/nn/scripts (or code/nn/server_scripts, the two folders have different parameter settings and path configurations) execute the script #### about data all the synthetic datasets have been pushed to this repo. Some large datasets are not available here.