Dragon is a distributed runtime environment with the goal of being a foundation for many libraries, applications, tools, and services. Software written on top of the Dragon runtime benefits from lower complexity, interoperability, scalability, and performance from "laptop to exaflop".
The Dragon project also includes implementations of strategically important APIs built on the Dragon runtime with the goal of making programming HPC systems fast, fun, and productive. The primary example of this is the standard Python multiprocessing API, which has driven many of the requirements on the Dragon runtime. Other APIs and services will come will be coming in the future.
The Dragon runtime builds from basic operating system (OS) features to maximize portability and incorporates numerous HPC techniques for performance and scalability. It provides a higher level abstraction of OS-like features including namespace, process and memory resource management, and communication across a distributed system or systems.
| Directory | Description |
|---|---|
| .devcontainer/ | Docker development environment (via [VS Code Remote Containers]) |
| doc/ | Internal and public documentation |
| examples/ | Examples and benchmarks for the Dragon API and Python multiprocessing |
| src/ | Source code and working development directory for all components |
| test/ | Unit tests for all components |
| external/ | External packages Dragon relies on |
The Dragon core requires only basic POSIX features in the OS, such as shared memory. Dragon should build and run on any modern Linux distribution. See src/requirements.txt for a breakdown of requirements and versions. Using the modulefiles included with it to set the enviroment requires environment-modules to be installed. Modules must also be initialized in your shell with a command like this (see CONTRIBUTING.md for a few more details):
. /usr/share/Modules/init/bash
To build a distribution package of Dragon that includes a Python wheel, run the following from the top-level repo directory:
cd src
module use $PWD/modulefiles
module load dragon-dev
make dist
This will produce a tarfile inside of src/dragon-dist. An error that may occur at this stage is
because the wheel Python package is not installed. You'll know if you see an error suggesting
python setup.py bdist_wheel fails. If you see that error, install the Python dependencies with
make install-pythondeps
To install the package from a new terminal:
tar -zxvf dragon-[rel info].tar.gz
cd dragon-[rel info]
pip install dragon-[rel info].whl
module use $PWD/modulefiles
module load dragon
You can then verify the install by running:
cd examples/multiprocessing
dragon p2p_lat.py --dragon
This should show output similar to the following if everything is setup correctly (note the actual latency numbers will not be the same):
$ dragon p2p_lat.py --dragon
dragon p2p_lat.py --dragon
using Dragon
Msglen [B] Lat [usec]
2 50.19235017243773
4 47.29981999844313
8 60.32353558111936
16 39.78859516791999
+++ head proc exited, code 0
Setting up a development environment can be done locally, similar to the package building steps above, or through a VSCode dev container. See CONTRIBUTING.md For details on using the VSCode container. The steps here are the minimal steps for getting going on a build so that you can then run tests. If you run into issues there are more hints and details in CONTRIBUTING.md.
Environment Setup and Building Dragon
The following lines assume that you have module commands configured. If you do not have them configured on your system you may need to source a line like the following to enable them before executing the other commands below.
. /opt/cray/pe/modules/default/init/bash
Begin by sourcing the setup script, which will enable the use of other scripts, such as dragon-config,
dragon-cleanup, and others.
. hack/setup
Configuring the Build for the High Speed Transport Agent (HSTA)
In order to configure the build and runtime environments in regards to external libraries, use
the dragon-config script. The --help option will give more details on its use.
When building for multi-node environments, make sure to specify the necessary include and lib directories
for the fabric backends using the dragon-config --add command with <backend>-include=<path to include dir>
and <backend>-build-lib=<path to build-time lib dir>, where <backend> can be ofi (Libfabric with the
CXI provider) or ucx (for Infiniband systems).
For running in multi-node environments, again use dragon-config --add, but this time using
<backend>-runtime-lib=<path to runtime lib dir>.
These can be combined into a single colon-separated list, e.g.:
dragon-config --add="ofi-include=/opt/cray/libfabric/1.15.2.0/include:ofi-build-lib=/opt/cray/libfabric/1.15.2.0/lib64:ofi-runtime-lib=/opt/cray/libfabric/1.15.2.0/lib64"
The current config is stored in ${HOME}/.dragon/dragon-config.json. You can serialize the current config into a colon-separated string using dragon-config --serialize, or clean it out using dragon-config --clean.
If the build or runtime environment is not configured for the use of HSTA, Dragon will fall back on the TCP transport agent.
Building
To completely build and set up the environment from scratch for single node execution, run these commands.
. hack/clean_build
Once you have built the package from scratch, new changes typically don't require a complete build. During development, smaller changes can be followed by a
. hack/build
Testing Dragon
Once Dragon is built, you can run the unit tests as follows:
cd ../test
make
This runs all Dragon unit tests (not including multiprocessing unit tests). To run all standard Python multiprocessing unit tests, follow these steps starting next to this README.md file:
cd examples/multiprocessing/unittests
make
In the event your experiment goes awry, we provide a helper script to clean up any zombie processes and memory:
dragon-cleanup
Refer to CONTRIBUTING.md on processes and requirements for contributing to Dragon.
The Dragon team is:
- Michael Burke [burke@hpe.com]
- Yian Chen [yian.chen@hpe.com]
- Eric Cozzi [eric.cozzi@hpe.com]
- Zach Crisler [zachary.crisler@hpe.com]
- Julius Donnert [julius.donnert@hpe.com]
- Veena Ghorakavi [veena.venkata.ghorakavi@hpe.com]
- Faisal Hadi (manager) [mohammad.hadi@hpe.com]
- Nick Hill [nicholas.hill@hpe.com]
- Maria Kalantzi [maria.kalantzi@hpe.com]
- Kent Lee [kent.lee@hpe.com]
- Pete Mendygral [pete.mendygral@hpe.com]
- Indira Pimpalkhare [indira.pimpalkhare@hpe.com]
- Davin Potts [davin.potts@hpe.com]
- Nick Radcliffe [nick.radcliffe@hpe.com]
- Colin Wahl [colin.wahl@hpe.com]