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[submodule "dace/external/cub"] | ||
path = dace/external/cub | ||
url = https://github.com/NVlabs/cub.git | ||
branch = 1.8.0 | ||
[submodule "dace/external/moodycamel"] | ||
path = dace/external/moodycamel | ||
url = https://github.com/cameron314/concurrentqueue.git | ||
[submodule "dace/external/hlslib"] | ||
path = dace/external/hlslib | ||
url = https://github.com/definelicht/hlslib.git |
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# dace | ||
DaCe - Data Centric Parallel Programming | ||
![D](dace.svg)aCe - Data-Centric Parallel Programming | ||
===================================================== | ||
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_Decoupling domain science from performance optimization._ | ||
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DaCe compiles code in various programming languages and paradigms (Python/Numpy, MATLAB, TensorFlow) and maps it efficiently to **CPUs, GPUs, and FPGAs** with high utilization, on par with the state-of-the-art. The key feature driving DaCe is its Stateful DataFlow multiGraph (SDFG) *data-centric intermediate representation*: A transformable, interactive representation of code based on data movement. | ||
With data-centric parallel programming, we enable **direct knowledge transfer** of performance optimization, regardless of the scientific application or the target processor. | ||
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DaCe can be written inline in Python and transformed in the command-line, or SDFGs can be interactively modified using the Data-centric Interactive Optimization Development Environment (DIODE). | ||
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For more information, see our [paper](http://www.arxiv.org/abs/1902.10345). | ||
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Tutorials | ||
--------- | ||
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* _Implicit Dataflow in Python (coming soon)_ | ||
* [Explicit Dataflow in Python](tutorials/explicit.ipynb) | ||
* [SDFG API](tutorials/sdfg_api.ipynb) | ||
* [Transformations](tutorials/transformations.ipynb) | ||
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Installation and Dependencies | ||
----------------------------- | ||
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To install: `pip install dace` | ||
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Runtime dependencies: | ||
* A C++14-capable compiler (e.g., gcc 5.3+) | ||
* Python 3.5 or newer | ||
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Running DIODE may require additional dependencies: | ||
* `sudo apt-get install libgtksourceviewmm-3.0-dev libyaml-dev` | ||
* `sudo apt-get install python3-cairo python3-gi-cairo libgirepository1.0-dev xdot libwebkitgtk-dev libwebkitgtk-3.0-dev libwebkit2gtk-4.0-dev` | ||
* `pip install pygobject matplotlib` | ||
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To run DIODE on Windows, use MSYS2: | ||
* Download from http://www.msys2.org/ | ||
* In the MSYS2 console, install all dependencies: `pacman -S mingw-w64-i686-gtk3 mingw-w64-i686-python2-gobject mingw-w64-i686-python3-gobject mingw-w64-i686-python3-cairo mingw-w64-i686-python3-pip mingw-w64-i686-gtksourceviewmm3 mingw-w64-i686-gcc mingw-w64-i686-boost mingw-w64-i686-python3-numpy mingw-w64-i686-python3-scipy mingw-w64-i686-python3-matplotlib` | ||
* Update MSYS2: `pacman -Syu`, close and restart MSYS2, then run `pacman -Su` to update the rest of the packages. | ||
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Publication | ||
----------- | ||
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If you use DaCe, cite us: | ||
```bibtex | ||
@article{dace, | ||
author = {Ben-Nun, Tal and de Fine Licht, Johannes and Ziogas, Alexandros Nikolaos and Schneider, Timo and Hoefler, Torsten}, | ||
title = {Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs}, | ||
journal = {CoRR}, | ||
volume = {abs/1902.10345}, | ||
year = {2019}, | ||
url = {http://arxiv.org/abs/1902.10345}, | ||
archivePrefix = {arXiv}, | ||
eprint = {1902.10345} | ||
} | ||
``` | ||
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Configuration | ||
------------- | ||
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DaCe creates a file called `.dace.conf` in the user's home directory. It provides useful settings that can be modified either directly in the file (YAML), within DIODE, or overriden on a case-by-case basis using environment variables that begin with `DACE_` and specify the setting (where categories are separated by underscores). The full configuration schema is located [here](dace/config_schema.yml). | ||
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Useful environment variable configurations include: | ||
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* `DACE_CONFIG` (default: `~/.dace.conf`): Override DaCe configuration file choice. | ||
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Context configuration: | ||
* `DACE_use_cache` (default: False): Uses DaCe program cache instead of re-optimizing and compiling programs. | ||
* `DACE_debugprint` (default: True): Print debugging information. | ||
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CPU target configuration: | ||
* `DACE_compiler_cpu_executable` (default: g++): Chooses the default C++ compiler for CPU code. | ||
* `DACE_compiler_cpu_additional_args` (default: None): Additional compiler flags (separated by spaces). | ||
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SDFG processing: | ||
* `DACE_optimizer_interface` (default: `dace.transformation.optimizer.SDFGOptimizer`): Controls the SDFG optimization process. If empty or class name is invalid, skips process. By default, uses the transformation command line interface. | ||
* `DACE_optimizer_visualize` (default: False): Visualizes optimization process by saving .dot (GraphViz) files after each pattern replacement. | ||
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Profiling: | ||
* `DACE_profiling` (default: False): Enables profiling measurement of the DaCe program runtime in milliseconds. Produces a log file and prints out median runtime. | ||
* `DACE_treps` (default: 100): Number of repetitions to run a DaCe program when profiling is enabled. | ||
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Contributing | ||
------------ | ||
DaCe is an open-source project. We are happy to accept Pull Requests with your contributions! | ||
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License | ||
------- | ||
DaCe is published under the New BSD license, see LICENSE. | ||
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from .types import * | ||
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# Python frontend | ||
from .frontend.python.decorators import * | ||
from .frontend.python.ndarray import * | ||
from .frontend.python.ndloop import ndrange | ||
from .frontend.python.simulator import simulate | ||
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from .config import Config | ||
from .frontend.operations import * | ||
from .sdfg import compile, SDFG, SDFGState | ||
from .memlet import Memlet, EmptyMemlet | ||
from .graph.edges import InterstateEdge | ||
from .symbolic import symbol, eval |
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