torcolvin / gpusimilarity

A Cuda/Thrust implementation of fingerprint similarity searching

gpusimilarity

A brute-force GPU implementation of chemical fingerprint similarity searching. Its intended use is to be kept alive as a service with an entire library loaded into graphics card memory. It has python scripts included which use RDKit to generate fingerprints, but the C++/Cuda backend are agnostic to the data once it's been created.

Architecture and benchmarks were presented in a presentation at the 2018 RDKit European UGM.

Basic Benchmark

On a machine with four Tesla V100, searching one billion compounds takes ~0.4 seconds.

See RDKit Presentation for much more in depth benchmarks.

Example integration

Here is a video of this backend being utilized for immediate-response searching inside Schrödinger's LiveDesign application:

Dependencies

• RDKit (At Python level, not compilation)
• Qt 5.2+ (including QtNetwork)
• PyQt
• Cuda SDK, CUDACXX env variable pointing to nvcc
• cmake 3.10.2+
• C++11 capable compiler
• Boost test libraries
• Optional: Doxygen for generating documents

Building with CMake and running unit tests with CTest

From parent directory of source:
mkdir bld
cd bld
ccmake ../gpusimilarity
make -j5
ctest

If Cuda, boost or doxygen are not found, start ccmake with the following options:

ccmake -DCMAKE_CUDA_COMPILER=/path/to/nvcc -DBOOST_ROOT=/path/to/boost/directory -DDOXYGEN_EXECUTABLE=/path/to/doxygen

Generate the documentation

Install doxygen on system

make doc_doxygen

The result is in bld/doc/html

Running

For basic json-response http endpoint:

From build directory: python3 ${SRC_DIR}/python/gpusim_server.py <fingerprint fsim file>

For testing (insecure):

From build directory: python3 ${SRC_DIR}/python/gpusim_server.py <fingerprint fsim file> --http_interface

For generating databases:

Easiest from rdkit conda with pyqt installed:

From source python directory: python3 gpusim_createdb.py <input smi.gz file> <fingerprint fsim file>

For debugging Cuda server, avoiding python/http server altogether:

From build directory:
./gpusimserver <dbname>.fsim
python3 python ${SRC_DIR}/python/gpusim_search.py <dbname>

Note: No .fsim extension is used for gpusim_search.py

This may be useful to determine if the backend is having Cuda/GPU problems.