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https://github.com/NVIDIA-Genomics-Research/GenomeWorks

SDK for GPU accelerated genome assembly and analysis
https://github.com/NVIDIA-Genomics-Research/GenomeWorks

alignment cuda genomics gpu mapping nvidia partial-order-alignment poa python-api

Last synced: 3 months ago
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SDK for GPU accelerated genome assembly and analysis

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README 

        
# GenomeWorks 



## Overview



GenomeWorks is a GPU-accelerated library for biological sequence analysis. This section provides a brief overview of the different components of GenomeWorks.

For more detailed API documentation please refer to the [documentation](#enable-doc-generation).



* Modules

    * [cudamapper](cudamapper/README.md) - CUDA-accelerated sequence to sequence mapping

    * [cudapoa](cudapoa/README.md) - CUDA-accelerated partial order alignment

    * [cudaaligner](cudaaligner/README.md) - CUDA-accelerated pairwise sequence alignment

    * [cudaextender](cudaextender/README.md) - CUDA-accelerated seed extension

* Setup GenomeWorks

    * [Clone GenomeWorks](#clone-genomeworks)

    * [System Requirements](#system-requirements)

    * [GenomeWorks Installation](#genomeworks-setup)

* [Python API](#genomeworks-python-api)

* [Development Support](#development-support)



## Clone GenomeWorks 



### Latest released version

This will clone the repo to the `master` branch, which contains code for latest released version

and hot-fixes.



```

git clone --recursive -b master https://github.com/clara-parabricks/GenomeWorks.git

```



### Latest development version

This will clone the repo to the default branch, which is set to be the latest development branch.

This branch is subject to change frequently as features and bug fixes are pushed.



```bash

git clone --recursive https://github.com/clara-parabricks/GenomeWorks.git

```



## System Requirements

Minimum requirements -



1. Ubuntu 16.04 or Ubuntu 18.04

2. CUDA 10.0+ (official instructions for installing CUDA are available [here](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html))

3. GPU generation Pascal and later (compute capability >= 6.0)

4. gcc/g++ 5.4.0+ / 7.x.x

5. Python 3.6.7+

6. CMake (>= 3.10.2)

7. autoconf (required to output SAM/BAM files)

8. automake (required to output SAM/BAM files)



## GenomeWorks Setup



### Build and Install

To build and install GenomeWorks -



```bash

mkdir build

cd build

cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=install -Dgw_cuda_gen_all_arch=OFF

make -j install

```



NOTE : The `gw_cuda_gen_all_arch=OFF` option pre-generates optimized code only for the GPU(s) on your system.

For building a binary that pre-generates opimized code for all common GPU architectures, please remove the option

or set it to `ON`.



NOTE : (OPTIONAL) To enable outputting overlaps in SAM/BAM format, pass the `gw_build_htslib=ON` option.



### Package generation

Package generation puts the libraries, headers and binaries built by the `make` command above

into a `.deb`/`.rpm` for portability and easy installation. The package generation itself doesn't

guarantee any cross-platform compatibility.



It is recommended that a separate build and packaging be performed for each distribution and

CUDA version that needs to be supported.



The type of package (deb vs rpm) is determined automatically based on the platform the code

is being run on. To generate a package for the SDK -



```bash

make package

```



## genomeworks Python API 

The python API for the GenomeWorks SDK is available through the `genomeworks` python package. More details

on how to use and develop `genomeworks` can be found in the README under `pygenomeworks` folder.



## Development Support

### Enable Unit Tests

To enable unit tests, add `-Dgw_enable_tests=ON` to the `cmake` command in the build step.



This builds GTest based unit tests for all applicable modules, and installs them under

`${CMAKE_INSTALL_PREFIX}/tests`. These tests are standalone binaries and can be executed

directly.

e.g.



```

cd $INSTALL_DIR

./tests/cudapoatests

```



### Enable Benchmarks

To enable benchmarks, add `-Dgw_enable_benchmarks=ON` to the `cmake` command in the build step.



This builds Google Benchmark based microbenchmarks for applicable modules. The built benchmarks

are installed under `${CMAKE_INSTALL_PREFIX}/benchmarks/` and can be run directly.



e.g.

```

#INSTALL_DIR/benchmarks/cudapoa/multibatch

```



A description of each of the benchmarks is present in a README under the module's benchmark folder.



### Enable Doc Generation

To enable document generation for GenomeWorks, please install `Doxygen` on your system.

Once`Doxygen` has been installed, run the following to build documents.



```bash

make docs

```



Docs are also generated as part of the default `all` target when `Doxygen` is available on the system.



To disable documentation generation add `-Dgw_generate_docs=OFF` to the `cmake` command in the [build step](#build).



### Code Formatting



GenomeWorks makes use of `clang-format` to format it's source and header files. To make use of

auto-formatting, `clang-format` would have to be installed from the LLVM package (for latest builds,

best to refer to http://releases.llvm.org/download.html).



Once `clang-format` has been installed, make sure the binary is in your path.



To add a folder to the auto-formatting list, use the macro `gw_enable_auto_formatting(FOLDER)`. This

will add all cpp source/header files to the formatting list.



To auto-format, run the following in your build directory.



```bash

make format

```



To check if files are correct formatted, run the following in your build directory.



```bash

make check-format

```



### Running CI Tests Locally

Please note, your git repository will be mounted to the container, any untracked files will be removed from it.

Before executing the CI locally, stash or add them to the index.



Requirements:

1. docker (https://docs.docker.com/install/linux/docker-ce/ubuntu/)

2. nvidia-docker (https://github.com/NVIDIA/nvidia-docker)

3. nvidia-container-runtime (https://github.com/NVIDIA/nvidia-container-runtime)



Run the following command to execute the CI build steps inside a container locally:

```bash

bash ci/local/build.sh -r 

```

ci/local/build.sh script was adapted from [rapidsai/cudf](https://github.com/rapidsai/cudf/tree/branch-0.11/ci/local)



The default docker image is **clara-genomics-base:cuda10.0-ubuntu16.04-gcc5-py3.7**.

Other images from [gpuci/clara-genomics-base](https://hub.docker.com/r/gpuci/clara-genomics-base/tags) repository can be used instead, by using -i argument

```bash

bash ci/local/build.sh -r  -i gpuci/clara-genomics-base:cuda10.0-ubuntu18.04-gcc7-py3.6

```