/QVina-W-GPU

A heterogeneous OpenCL implementation of QuickVina-W

Primary LanguageC++

QVina-W-GPU

QVina-W-GPU is proposed to parallelize the efficient QVina-W docking software, which can exploit the highly-parallel hardware architecture to speed up the process of molecule docking.

Compiling and Running

Note: at least one GPU card is required and make sure the version of GPU driver is up to date

Windows

Run on the executable file

  1. For the first time to use QVina-GPU, please run QVina-W-GPU-K.exe with command ./Qvina-W-GPU-K.exe --config ./input_file_example/2bm2_config.txt You are supposed to have the docking results 2bm2_out.pdbqt of our example complex and a Kernel2_Opt.bin file

  2. Once you have the Kernel2_Opt.bin file, you can run Qvina-W-GPU.exe without compiling the kernel files (thus to save more runtime)

    When you run Qvina-W-GPU.exe, please make sure Kernel2_Opt.bin file are in the same directory

Build from source file

Visual Studio 2019 is recommended for build Vina-GPU from source

  1. install boost library (current version is 1.77.0)

  2. install CUDA Toolkit (current version is v11.5) if you are using NVIDIA GPU cards

    Note: the OpenCL library can be found in CUDA installation path for NVIDIA or in the driver installation path for AMD

  3. add ./lib ./OpenCL/inc $(YOUR_BOOST_LIBRARY_PATH)/boost $(YOUR_CUDA_TOOLKIT_LIBRARY_PATH)/CUDA/v11.5/include in the include directories

  4. add $(YOUR_BOOST_LIBRARY_PATH)/stage/lib $(YOUR_CUDA_TOOLKIT_PATH)/CUDA/lib/Win32in the addtional library

  5. add OpenCL.lib in the additional dependencies

  6. add --config ./input_file_example/2bm2_config.txt in the command arguments

  7. add WIN32 in the preprocessor definitions if necessary

  8. if you want to compile the binary kernel file on the fly, add BUILD_KERNEL_FROM_SOURCE in the preprocessor definitions

  9. build & run

Note: ensure the line ending are CLRF

Linux

Note: At least 8M stack size is needed. To change the stack size, use ulimit -s 8192.

  1. install boost library (current version is 1.77.0)

  2. install CUDA Toolkit (current version is 11.5) if you are using NVIDIA GPU cards

    note: OpenCL library can be usually in /usr/local/cuda (for NVIDIA GPU cards)

  3. change the BOOST_LIB_PATH and OPENCL_LIB_PATH accordingly in Makefile

  4. set GPU platform GPU_PLATFORM and OpenCL version OPENCL_VERSIONin Makefile. some options are given below:

    Note: -DOPENCL_3_0 is highly recommended in Linux. To check the OpenCL version on a given platform, use clinfo.

Macros Options Descriptions
GPU_PLATFORM -DNVIDIA_PLATFORM / -DAMD_PLATFORM NVIDIA / AMD GPU platform
OPENCL_VERSION -DOPENCL_3_0 / -DOPENCL_1_2 OpenCL version 3.0 / 1.2

  1. type make clean and make source to build Vina-GPU that compile the kernel files on the fly (this would take some time at the first use)

  2. after a successful compiling, Vina-GPU can be seen in the directory

  3. type ./QVina-W-GPU --config ./input_file_example/2bm2_config.txt to run QVina-W-GPU

  4. once you successfully run QVina-W-GPU, its runtime can be further reduced by typing make clean and make to build it without compiling kernel files (but make sure the Kernel2_Opt.bin file is unchanged)

  5. other compile options:

Options Description
-g debug
-DDISPLAY_ADDITION_INFO print addition information