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Getting started

This section gives instructions to build the library from sources. These steps are required if you want to build library for your specific platform with custom build settings.

Requirements

  • Linux-based OS (tested on Ubuntu 20.04)
  • CMake Version 3.15 or higher
  • CUDA Compatible GPU device (to run Cuda computations)
  • GCC Compiler
  • NVIDIA CUDA toolkit (to build Cuda backend)
  • Python 3 (for pyspbla library)
  • Git (to get source code)

Cuda & compiler setup

Skip this section if you want to build library with only sequential backend without cuda backend support.

Before the CUDA setup process, validate your system NVIDIA driver with nvidia-smi command. Install required driver via ubuntu-drivers devices and apt install <driver> commands respectively.

The following commands grubs the required GCC compilers for the CC and CXX compiling respectively. CUDA toolkit, shipped in the default Ubuntu package manager, has version number 10 and supports only GCC of the version 8.4 or less.

$ sudo apt update
$ sudo apt install gcc-8 g++-8
$ sudo apt install nvidia-cuda-toolkit
$ sudo apt install nvidia-cuda-dev 
$ nvcc --version

If everything successfully installed, the last version command will output something like this:

$ nvcc: NVIDIA (R) Cuda compiler driver
$ Copyright (c) 2005-2019 NVIDIA Corporation
$ Built on Sun_Jul_28_19:07:16_PDT_2019
$ Cuda compilation tools, release 10.1, V10.1.243

Bonus Step: In order to have CUDA support in the CLion IDE, you will have to overwrite global alias for the gcc and g++ compilers:

$ sudo rm /usr/bin/gcc
$ sudo rm /usr/bin/g++
$ sudo ln -s /usr/bin/gcc-8 /usr/bin/gcc
$ sudo ln -s /usr/bin/g++-8 /usr/bin/g++

This step can be easily undone by removing old aliases and creating new one for the desired gcc version on your machine. Also you can safely omit this step if you want to build library from the command line only.

Useful links:

Get the source code and run

Run the following commands in the command shell to download the repository, make build directory, configure cmake build and run compilation process. First of all, get the source code and project dependencies:

$ git clone https://github.com/JetBrains-Research/spbla.git
$ cd spbla
$ git submodule update --init --recursive

Make the build directory and go into it:

$ mkdir build
$ cd build

Configure build in Release mode with tests and run actual compilation process:

$ cmake .. -DCMAKE_BUILD_TYPE=Release -DSPBLA_BUILD_TESTS=ON
$ cmake --build . --target all -j `nproc`
$ bash ./scripts/run_tests_all.sh

By default, the following cmake options will be automatically enabled:

  • SPBLA_WITH_CUDA - build library with actual cuda backend
  • SPBLA_WITH_OPENCL - build library with actual cuda backend
  • SPBLA_WITH_SEQUENTIAL - build library witt cpu based backend
  • SPBLA_WITH_TESTS - build library unit-tests collection
  • SPBLA_WITH_CUB - build library with bundled CUB sources, relevant for CUDA SDK 10 and earlier

Note: in order to provide correct GCC version for CUDA sources compiling, you will have to provide custom paths to the CC and CXX compilers before the actual compilation process as follows:

$ export CC=/usr/bin/gcc-8
$ export CXX=/usr/bin/g++-8
$ export CUDAHOSTCXX=/usr/bin/g++-8

Python package

Export env variable PYTHONPATH="/build_dir_path/python/:$PYTHONPATH" if you want to use pyspbla without installation into default python packages dir. This variable will help python find package if you import it as import pyspbla in your python scripts.

Tests

To run regression tests within your build directory, open folder /build_dir_path/python and run the following command:

$ export PYTHONPATH="`pwd`:$PYTHONPATH"
$ cd tests
$ python3 -m unittest discover -v

Note: after the build process, the shared library object will be placed inside the build directory in the folder with python wrapper python/pyspbla/. So, the wrapper will be able to automatically locate required lib file.

Package config

You can configure python package by the usage of the following optional env variables:

  • SPBLA_PATH - path to the compiled spbla library. Setup this variable, if you want to use your custom library build. Setup this variable as /path/to/the/compiled/library/libspbla.so (actual lib name depend on target platform).

  • SPBLA_BACKEND - string name of the preferred backend for computations. Allowed options are default (default backend will be selected), cpu, cuda and opencl.

Following example shows how to configure these variables within Python runtime:

# import os
# os.environ["SPBLA_BACKEND"] = "cpu"
# os.environ["SPBLA_BACKEND"] = "cuda"
# os.environ["SPBLA_BACKEND"] = "opencl"

# Uncomment desired line to setup selected backend before actual package import
import pyspbla as sp