scikit-bio-binaries is an open-source, BSD-licensed package providing optimized algorithms for bioinformatics in support of scikit-bio.

You can install the latest release of scikit-bio-binaries using conda (https://www.anaconda.com/docs/getting-started/miniconda/main):

conda install -c conda-forge scikit-bio-binaries

Alternatively, in the near future you will be able to install the latest release of scikit-bio-binaries using pip:

pip install scikit-bio-binaries

The package can be build from source on your local machine. We recommend using the conda-provided compilers and libraries, but system-installed ones should work as well.

If you decide to create a dedicated build environment in conda (https://www.anaconda.com/docs/getting-started/miniconda/main):

cplatform=`conda info |awk '/platform/{print $3}'`
if [[ "$(uname -s)" == "Linux" ]];
then
  conda create -n skbb-build -c conda-forge gxx_${cplatform}
else
  conda create -n skbb-build -c conda-forge clangxx_${cplatform}
fi
conda activate skbb-build
conda install -c conda-forge libcblas liblapacke blas-devel make
make clean && make clean_install && make all

To test that the build succeeded, run:

make test

To build NVIDIA-GPU-enabled code, you will also need the CUDA compiler. On Linux, you can install it from conda with:

conda install -c conda-forge cuda-compiler

The CUDA build is not enabled by default. If you are interested in building the GPU-accelerated libraries, set:

export NV_CUDA=Y

To build AMD-GPU-enabled code, you will also need the HIP compiler.

The HIP build is not enabled by default. If you are interested in building the GPU-accelerated libraries, set:

export AMD_HIP=Y

There is also support for OpenMP Offload and OpenACC builds, by setting either AMD_CXX or NV_CXX, but it is still experimental.

The functions provided by scikit-bio-binaries are packaged as a shared library and will be installed in:

$CONDA_PREFIX/lib/libskbb.so

If GPU code was build, you will also have:

$CONDA_PREFIX/lib/libskbb_acc_nv.so

The C header files that describe how to access them are avaialable in src/extern/, but will also be installed in:

$CONDA_PREFIX/include/scikit-bio-binaries/

Compiled languages can use the provided C headers during compilation, and link against the provided shared library. See api_tests/ for an example:

$(CC) $(CFLAGS) my_code.c $(LDFLAGS) -lskbb -o my_exe

Python users can instead use the ctypes module to import the shared library at runtime. No header files are needed (or provided), but you can use the C headers to guide your implementation. For example:

import ctypes
dll = ctypes.CDLL("libskbb.so")
skbb_version = dll.skbb_get_api_version()

This package uses OpenMP to make use of multiple CPU cores. By default, scikit-bio-binaries will use all the cores that are available on the system. To restrict the number of cores used, set:

export OMP_NUM_THREADS=nthreads

On x86_64 based CPU platforms, scikit-bio-binaries will auto-detect the CPU generation, i.e. if it supports avx2 or avx512 vector instructions. To force the most compatible binary variant, one can set:

export SKBB_MAX_CPU=basic

To check which binary is used (scikit-bio-binaries will print it to standard output at runtime), set:

export SKBB_CPU_INFO=Y

If the code has been compiled for GPUs, scikit-bio-binaries will auto-detect the presence of either NVIDIA or AMD GPUs, and use such a GPU for the GPU-enabled algorithms. To force CPU-only compute, one can set:

export SKBB_USE_GPU=N

To check if a GPU is used (scikit-bio-binaries will print it to standard output at runtime), set:

export SKBB_GPU_INFO=Y

When evaluating the performance of scikit-bio-binaries it is sometimes necessary to distinguish the time spent interacting with the data from the compute proper. Additional informational messages can be enabled by setting:

export SKBB_TIMING_INFO=Y

In the near future, scikit-bio-binaries will be used by scikit-bio and unifrac-binaries.

scikit-bio-binaries is available under the new BSD license. See LICENSE.txt for scikit-bio's license.

The library is currently mainatined by Igor Sfiligoi at the University of California San Diego (UCSD) (@sfiligoi). Guidance and support is also provided by Dr. Qiyun Zhu at Arizona State University (ASU) (@qiyunzhu), Dr. Daniel McDonald at the University of California San Diego (UCSD) (@wasade), and Dr. Rob Knight at the University of California San Diego (UCSD) (@rob-knight).

The algorithms in this package are based on code developped as part of the scikit-bio (https://scikit.bio) package. See the main scikit-bio page for credits about the original algorithm contributers.

The development of scikit-bio is currently supported by the U.S. Department of Energy, Office of Science under award number DE-SC0024320, awarded to Dr. Qiyun Zhu at ASU (lead PI), Dr. James Morton at Gutz Analytics, and Dr. Rob Knight at UCSD.

If you use scikit-bio derived code, including scikit-bio-binaries, for any published research, please see our Zenodo page for how to cite.

The logo of scikit-bio was created by Alina Prassas. Vector and bitmap image files are available at the logos directory.