Parallel, distributed NumPy-like arrays backed by Chapel
Arkouda (αρκούδα): NumPy-like arrays at massive scale backed by Chapel.
NOTE: Arkouda is under the MIT license.
Talks on Arkouda
Exploratory data analysis (EDA) is a prerequisite for all data science, as illustrated by the ubiquity of Jupyter notebooks, the preferred interface for EDA among data scientists. The operations involved in exploring and transforming the data are often at least as computationally intensive as downstream applications (e.g. machine learning algorithms), and as datasets grow, so does the need for HPC-enabled EDA. However, the inherently interactive and open-ended nature of EDA does not mesh well with current HPC usage models. Meanwhile, several existing projects from outside the traditional HPC space attempt to combine interactivity and distributed computation using programming paradigms and tools from cloud computing, but none of these projects have come close to meeting our needs for high-performance EDA.
To fill this gap, we have developed a software package, called Arkouda, which allows a user to interactively issue massively parallel computations on distributed data using functions and syntax that mimic NumPy, the underlying computational library used in the vast majority of Python data science workflows. The computational heart of Arkouda is a Chapel interpreter that accepts a pre-defined set of commands from a client (currently implemented in Python) and uses Chapel's built-in machinery for multi-locale and multithreaded execution. Arkouda has benefited greatly from Chapel's distinctive features and has also helped guide the development of the language.
In early applications, users of Arkouda have tended to iterate rapidly between multi-node execution with Arkouda and single-node analysis in Python, relying on Arkouda to filter a large dataset down to a smaller collection suitable for analysis in Python, and then feeding the results back into Arkouda computations on the full dataset. This paradigm has already proved very fruitful for EDA. Our goal is to enable users to progress seamlessly from EDA to specialized algorithms by making Arkouda an integration point for HPC implementations of expensive kernels like FFTs, sparse linear algebra, and graph traversal. With Arkouda serving the role of a shell, a data scientist could explore, prepare, and call optimized HPC libraries on massive datasets, all within the same interactive session.
- requires chapel 1.22.0
- requires zeromq version >= 4.2.5, tested with 4.2.5 and 4.3.1
- requires hdf5
- requires python 3.6 or greater
- requires numpy
- requires Sphinx and sphinx-argparse to build python documentation
- requires pytest and pytest-env to execute the Python test harness
MacOS Environment Installation
It is usually very simple to get things going on a mac:
brew install zeromq brew install hdf5 brew install chapel # Although not required, is is highly recommended to install Anaconda to provide a # Python 3 environment and manage Python dependencies: wget https://repo.anaconda.com/archive/Anaconda3-2020.02-Linux-x86_64.sh sh Anaconda3-2020.02-Linux-x86_64.sh source ~/.bashrc # Otherwise, Python 3 can be installed with brew brew install python3 # !!! the standard way of installing through pip3 installs an old version of arkouda # !!! the arkouda python client is available via pip # !!! pip will automatically install python dependencies (zmq and numpy) # !!! however, pip will not build the arkouda server (see below) # !!!pip3 install arkouda # # install the version of the arkouda python package which came with the arkouda_server # if you plan on editing the arkouda python package use the -e flag # from the local arkouda repo/directory run... pip3 install -e . # # these packages are nice but not a requirement pip3 install pandas pip3 install jupyter
If it is preferred to build Chapel instead of using the brew install, the process is as follows:
# on my mac build chapel in my home directory with these settings... export CHPL_HOME=~/chapel/chapel-1.22.0 source $CHPL_HOME/util/setchplenv.bash export CHPL_COMM=gasnet export CHPL_COMM_SUBSTRATE=smp export CHPL_TARGET_CPU=native export GASNET_QUIET=Y export CHPL_RT_OVERSUBSCRIBED=yes cd $CHPL_HOME make # Build chpldoc to enable generation of Arkouda docs make chpldoc # Add the Chapel executable (chpl) to PATH either in ~/.bashrc (single user) # or /etc/environment (all users): export PATH=$CHPL_HOME/bin/linux64-x86_64/:$PATH
Linux Environment Installation
There is no Linux Chapel install, so the first two steps in the Linux Arkouda install are to install the Chapel dependencies followed by downloading and building Chapel:
# Update Linux kernel and install Chapel dependencies sudo apt-get update sudo apt-get install gcc g++ m4 perl python python-dev python-setuptools bash make mawk git pkg-config # Download latest Chapel release, explode archive, and navigate to source root directory wget https://github.com/chapel-lang/chapel/releases/download/1.22.0/chapel-1.22.0.tar.gz tar xvf chapel-1.22.0.tar.gz cd chapel-1.22.0/ # Set CHPL_HOME export CHPL_HOME=$PWD # Add chpl to PATH source $CHPL_HOME/util/setchplenv.bash # Set remaining env variables and execute make export CHPL_COMM=gasnet export CHPL_COMM_SUBSTRATE=smp export CHPL_TARGET_CPU=native export GASNET_QUIET=Y export CHPL_RT_OVERSUBSCRIBED=yes cd $CHPL_HOME make # Build chpldoc to enable generation of Arkouda docs make chpldoc # Optionally add the Chapel executable (chpl) to the PATH for all users: /etc/environment export PATH=$CHPL_HOME/bin/linux64-x86_64/:$PATH
As is the case with the MacOS install, it is highly recommended to install Anaconda to provide a Python environment and manage Python dependencies:
wget https://repo.anaconda.com/archive/Anaconda3-2020.02-Linux-x86_64.sh sh Anaconda3-2020.02-Linux-x86_64.sh source ~/.bashrc
Download, clone, or fork the arkouda repo. Further instructions assume that the current directory is the top-level directory of the repo.
If your environment requires non-system paths to find dependencies (e.g.,
if using the ZMQ and HDF5 bundled with [Anaconda]), append each path to a new file
Makefile.paths like so:
# Makefile.paths # Custom Anaconda environment for Arkouda $(eval $(call add-path,/home/user/anaconda3/envs/arkouda)) # ^ Note: No space after comma.
chpl compiler will be executed with
-L and an
-rpath to each path.
# If zmq and hdf5 have not been installed previously, execute make install-deps make install-deps # Run make to build the arkouda_server executable make
Now that the arkouda_server is built and tested, install the Python library
Installing the Arkouda Python Library
pip3 install -e .
There are two unit test suites for Arkouda, one for Python and one for Chapel. As mentioned above, the Arkouda Python test harness leverages the pytest and pytest-env libraries, whereas the Chapel test harness does not require any external librares.
Building the Arkouda documentation
Make sure you've installed the Sphinx and sphinx-argparse packages (e.g.
pip3 install -U Sphinx sphinx-argparse). Important: if you've built Chapel, you must execute make chpldoc as detailed above.
make doc to build both the Arkouda python documentation and the Chapel server documentation
The output is currently in subdirectories of the
arkouda/doc/python # python frontend documentation arkouda/doc/server # chapel backend server documentation
To view the documentation for the Arkouda python client, point your browser to
substituting the appropriate path for your configuration.
The command-line invocation depends on whether you built a single-locale version (with
multi-locale version (with
Multi-locale startup (user selects the number of locales):
./arkouda_server -nl 2
Also can run server with memory checking turned on using
By default, the server listens on port
5555 and prints verbose output. These options can be changed with command-line
Memory checking is turned off by default and turned on by using
Logging messages are turned on by default and turned off by using
Verbose messages are turned on by default and turned off by using
Other command line options are available, view them by using
To sanity check the arkouda server, you can run
This will start the server, run a few computations, and shut the server down. In addition, the check script can be executed against a running server by running the following Python command:
python3 tests/check.py localhost 5555
Contributing to Arkouda
If you'd like to contribute, please see CONTRIBUTING.md.
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