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GENIE

GENIE is a Generic Inverted Index on GPU. It builds a database (inverted index) from high dimensional data, commonly preprocessed by either Locality Sensitive Hashing or Shotgun and Assembly schemes. GENIE provides a simple way to perform top-k similarity queries on top of such inverted index. The user may define queries as dimension and value pairs, and optionally value ranges and weights. GENIE processes all queries in parallel on GPU using a Match Count similarity model (number of dimensions with matching values in a query). For each query, top-k similar results and their corresponding counts are returned. GENIE is much faster than other CPU searching algorithms due to extensive parallelism on two levels: parallel query processing and multiple queries processed in parallel.

Please refer to the following papers:

  • ICDE2018 paper: Jingbo Zhou, Qi Guo, H. V. Jagadish, Lubos Krcaly, Siyuan Liu, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng; A Generic Inverted Index Framework for Similarity Search on the GPU; IEEE International Conference on Data Engineering, 2018 (ICDE 2018).

  • Technical Report: CoRR arXiv:1603.08390 at https://arxiv.org/abs/1603.08390

Compilation and development

You are required to install G++, CMake, CUDA, OpenMPI and Boost. The minimum required versions are:

  • GCC with C++11 support (4.8)
  • CMake 3.8
  • CUDA 7.0
  • OpenMPI 1.7 (for GENIE_DISTRIBUTED only)
  • Boost 1.63: serialization, iostreams, program_options (for GENIE_COMPR only)

To create an "out-of-source" build of GENIE containing both the GENIE library, tests and tools, you can use the standard CMake procedure:

$ mkdir build
$ cd build
$ cmake ..
$ make -j8

Use target $ make test to run GENIE tests, $ make doc to build html code documentation, $ make install to install GENIE.

CMake build parameters can be further configured using the following options:

  • CMAKE_BUILD_TYPE:STRING -- build type, one of Release, Debug (default Release)
  • CMAKE_INSTALL_PREFIX:PATH -- cmake's option for installation prefix (default ${CMAKE_BINARY_DIR}/install)
  • BOOST_ROOT:PATH -- root dir of Boost libraries (default from system paths)
  • DOXYGEN_EXECUTABLE:PATH -- doxygen executable (default from system paths)
  • MPI_HOME:PATH -- root dir of OpenMPI installation (default from system paths)
  • GENIE_DISTRIBUTED:BOOL -- enable distributed GENIE module (default OFF)
  • GENIE_COMPR:BOOL -- enable compression GENIE module (default OFF)
  • GENIE_SIMDCAI:BOOL -- enable compilation of SIMDCAI library (default OFF)
  • GENIE_EXAMPLES:BOOL -- enable compilation of GENIE examples (default ON)

Example use of cmake command may look like this:

$ cmake -DGENIE_SIMDCAI=ON -DCMAKE_BUILD_TYPE=Release -DGENIE_DISTRIBUTED=ON -DGENIE_COMPR=ON \
        -DBOOST_ROOT=/home/lubos/boost -DCMAKE_INSTALL_PREFIX=/home/lubos/genie-install ..

Running GENIE

There are several main parts of the GENIE project. The core is a library /lib/libgenie.a with the main functionality. To see how to use the library, you can check the source code in either /example or /test directories. Tests are the simplest applications built on top of GENIE library. Other utilities include a compression performance toolkit in /perf and miscellaneous utilities in /utility. All of these tools are compiled into /bin directory.

Compression performance toolkit

Compression toolkit compression is compiled into bin directory. It's a standalone app for performance measurements of GENIE focused mainly on compression capability.

To see all options of the compression performance toolkit, run:

$ ./perftoolkit --help

Multi-node Multi-GPU example

The current version supports loading multiple tables (i.e. clusters) and executing queries on corresponding clusters.

Preparing dataset and queries

Dataset

First, you need to cluster a single .csv file into multiple files with one cluster of data in each file. Make sure the files have a common prefix in their file names and that the file names end with _<cluster-id> (e.g. sift_0.csv, sift_1.csv).

After the clustering, you need to further split each file into multiple files for loading onto multiple GPUs. This could be done with the split.sh script in the utility folder (currently it splits into 2 files for 2 GPUs). If a cluster is named sift_0.csv, this will split it into sift_0_0.csv and sift_0_1.csv.

Once the files are ready, you may want to convert them into binary format for faster loading (if you want to experiment a few times, this greatly speeds up the loading process for subsequent runs). The conversion could be done by the csv2binary program in bin. A helper script, convert.sh is also provided to convert multiple files at once. For example, given 20 clusters and 2 GPUs (with prefix sift), we can do

$ bash convert.sh 20 2 sift

To make sure everything works, please place the above mentioned programs/scripts and your clustered .csv files into a single directory before processing the files.

Queries

The query is in JSON format, for 2 queries of dimension 5, you do

{
  "topk": 10,
  "queries": [
    {
      "content": [1, 2, 3, 4, 5],
      "clusters": [0, 9, 13]
    },
    {
      "content": [90, 24, 33, 14, 5],
      "clusters": [3, 7]
    }
  ]
}

This sends query 1 2 3 4 5 and 90 24 33 14 5 with topk set to 10. Currently the user needs to specify the clusters to search for a given query.

Running MPIGenie

To run MPIGenie on a single node, use

$ mpirun -np <n> ./bin/odgenie static/online.config.json

To run MPIGenie on multiple nodes, use

$ /path/to/mpirun -np <n> -hostfile hosts ./bin/odgenie static/online.config.json

An example hosts file looks like

slave1 slots=3
slave2 slots=3
slave3 slots=3
slave4 slots=3

Sometimes, you may need to specify which network interface to use

$ /path/to/mpi -np <n> -hostfile hosts --mca btl_tcp_if_include <interface> ./bin/odgenie static/online.config.json

Modify the configuration file accordingly. If you converted files into binary format, set data_format to be 1 in the configuration file.

The program will listen for question on port 9090. You can send queries to the program by executing

$ nc <hostname> 9090 < queries.json

Debugging

Local Debugging

Use cuda-gdb the same way you would use gdb. Example workflow of debugging the compression toolset may look like this:

$ cd debug && cmake -DCMAKE_BUILD_TYPE=Release .. && make && cd bin
$ rm -rf log && cuda-gdb -ex "source .b" --args ./compression integrated /home/lubos/data/adult.csv all

where ".b" contains gdb breakpoints and cuda-gdb autosteps (using save b .b from gdb or cuda-gdb).

Distributed GENIE

Run MPI with ENABLE_GDB=1 environment variable:

$ mpirun -np 2 -x ENABLE_GDB=1 ./bin/odgenie ./static/online.config.json

If there is only one batch of MPI processes running, we can find PID automatically. Note that we need to set variable i to non-zero value to start the process after we have attached all gdbs we want.

$ pid=$(pgrep odgenie | sed -n 1p); gdb -q --pid "${pid}" -ex "up 100" -ex "down 1" -ex "set variable gdb_attached=1" -ex "continue"

To attach other processes, use their corresponding PID (PID of rank 0 process + rank). Do this before starting the rank 0 process by setting variable i.

$ pid=$(pgrep odgenie | sed -n 2p); gdb -q --pid "${pid}"

Documentation

The documentation is available online at http://sesame-nus.github.io/genie.

Code documentation for GENIE can be generated with cmake and make. After you configure CMake following steps in Compilation and Development, just run $ make doc.