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extrap-4.0.4
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مشاهده بیشترتوضیحات
Extra-P, automated performance modeling for HPC applications
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | extrap-4.0.4 |
| نام | extrap |
| نسخه کتابخانه | 4.0.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Extra-P project |
| ایمیل نویسنده | extra-p@lists.parallel.informatik.tu-darmstadt.de |
| آدرس صفحه اصلی | https://github.com/extra-p/extrap |
| آدرس اینترنتی | https://pypi.org/project/extrap/ |
| مجوز | - |
# Extra-P
**Automated performance modeling for HPC applications**
[](https://badge.fury.io/py/extrap)
[](https://badge.fury.io/py/extrap)
[](https://badge.fury.io/py/extrap)
[<img alt="Screenshot of Extra-P" src="https://github.com/extra-p/extrap/raw/master/docs/images/extra-p-2d.png" height="200" align="right" title="Screenshot of Extra-P"/>](docs/images/extra-p-2d.png)
Extra-P is an automatic performance-modeling tool that supports the user in the identification of *scalability bugs*.
A scalability bug is a part of the program whose scaling behavior is unintentionally poor,
that is, much worse than expected. A performance model is a formula that expresses a performance metric of interest
such as execution time or energy consumption as a function of one or more execution parameters such as the size of the
input problem or the number of processors.
Extra-P uses measurements of various performance metrics at different execution configurations as input to generate
performance models of code regions (including their calling context) as a function of the execution parameters.
All it takes to search for scalability issues even in full-blown codes is to run a manageable number of small-scale
performance experiments, launch Extra-P, and compare the asymptotic or extrapolated performance of the worst instances
to the expectations.
Extra-P generates not only a list of potential scalability bugs but also human-readable models for all
performance metrics available such as floating-point operations or bytes sent by MPI calls that can be further
analyzed and compared to identify the root causes of scalability issues.
Extra-P is developed by [TU Darmstadt](https://www.parallel.informatik.tu-darmstadt.de/) –
in collaboration with [ETH Zurich](https://spcl.inf.ethz.ch/).
*For questions regarding Extra-P please send a message to <extra-p-support@lists.parallel.informatik.tu-darmstadt.de>.*
--------------------------------------------------------------------------------------------
### Table of Contents
1. [Requirements](#Requirements)
2. [Installation](#Installation)
3. [How to use it](#Usage)
4. [License](#License)
5. [Citation](#Citation)
6. [Publications](#Publications)
--------------------------------------------------------------------------------------------
### Requirements
* Python 3.7 or higher
* numpy
* pycubexr
* marshmallow
* packaging
* tqdm
* PySide2 (for GUI)
* matplotlib (for GUI)
* pyobjc-framework-Cocoa (only for GUI on macOS)
### Installation
Use the following command to install Extra-P and all required packages via `pip`.
```
python -m pip install extrap --upgrade
```
The `--upgrade` forces the installation of a new version if a previous version is already installed.
### Usage
Extra-P can be used in two ways, either using the command-line interface or the graphical user interface. More
information about the usage of Extra-P with both interfaces can be found in the [quick start guide](docs/quick-start.md)
.
#### Graphical user interface
The graphical user interface can be started by executing the `extrap-gui` command.
#### Command line interface
The command line interface is available under the `extrap` command:
`extrap` _OPTIONS_ (`--cube` | `--text` | `--talpas` | `--json` | `--extra-p-3`) _FILEPATH_
You can use different input formats as shown in the examples below:
* Text files: `extrap --text test/data/text/one_parameter_1.txt`
* JSON files: `extrap --json test/data/json/input_1.JSON`
* Talpas files: `extrap --talpas test/data/talpas/talpas_1.txt`
* Create model and save it to text file at the given
path: `extrap --out test.txt --text test/data/text/one_parameter_1.txt`
The Extra-P command line interface has the following options.
| Arguments | |
|----------------------------------------------------------------------|----------------------------------------------|
| **Positional** | |
| _FILEPATH_ | Specify a file path for Extra-P to work with |
| **Optional** | |
| `-h`, `--help` | Show help message and exit |
| `--version` | Show program's version number and exit |
| `--log` {`debug`, `info`, `warning`, `error`, `critical`} | Set program's log level (default: `warning`) |
| **Input options** | |
| `--cube` | Load data from CUBE files |
| `--text` | Load data from text files |
| `--talpas` | Load data from Talpas data format |
| `--json` | Load data from JSON or JSON Lines file |
| `--extra-p-3` | Load data from Extra-P 3 experiment |
| `--scaling` {`weak`, `strong`} | Set weak or strong scaling when loading data from CUBE files (default: `weak`) |
| **Modeling options** | |
| `--median` | Use median values for computation instead of mean values |
| `--modeler` {`default`, `basic`, `refining`, `multi-parameter`} | Selects the modeler for generating the performance models |
| `--options` _KEY_=_VALUE_ [_KEY_=_VALUE_ ...] | Options for the selected modeler |
| `--help-modeler` {`default`, `basic`, `refining`, `multi-parameter`} | Show help for modeler options and exit |
| **Output options** | |
| `--out` _OUTPUT_PATH_ | Specify the output path for Extra-P results |
| `--print` {`all`, `callpaths`, `metrics`, `parameters`, `functions`} | Set which information should be displayed after modeling (default: `all`) |
| `--save-experiment` <i>EXPERIMENT_PATH</i> | Saves the experiment including all models as Extra-P experiment (if no extension is specified, “.extra-p” is appended) |
### License
[BSD 3-Clause "New" or "Revised" License](LICENSE)
### Citation
Please cite Extra-P in your publications if it helps your research:
@inproceedings{calotoiu_ea:2013:modeling,
author = {Calotoiu, Alexandru and Hoefler, Torsten and Poke, Marius and Wolf, Felix},
month = {November},
title = {Using Automated Performance Modeling to Find Scalability Bugs in Complex Codes},
booktitle = {Proc. of the ACM/IEEE Conference on Supercomputing (SC13), Denver, CO, USA},
year = {2013},
pages = {1--12},
publisher = {ACM},
isbn = {978-1-4503-2378-9},
doi = {10.1145/2503210.2503277}
}
### Publications
1. Alexandru Calotoiu, David Beckingsale, Christopher W. Earl, Torsten Hoefler, Ian Karlin, Martin Schulz, Felix Wolf: Fast Multi-Parameter Performance Modeling. In Proc. of the 2016 IEEE International Conference on Cluster Computing (CLUSTER), Taipei, Taiwan, pages 172–181, IEEE, September 2016. [PDF](https://apps.fz-juelich.de/jsc-pubsystem/aigaion/attachments/fastmultiparam.pdf-f839eba376c6d61a8c4cab9860b6b3bf.pdf)
2. Marcus Ritter, Alexandru Calotoiu, Sebastian Rinke, Thorsten Reimann, Torsten Hoefler, Felix Wolf: *Learning Cost-Effective Sampling Strategies for Empirical Performance Modeling.* In Proc. of the 34th IEEE International Parallel and Distributed Processing Symposium (IPDPS), New Orleans, LA, USA, pages 884–895, IEEE, May 2020. [PDF](https://apps.fz-juelich.de/jsc-pubsystem/aigaion/attachments/ritter_ea_2020_ipdps.pdf-01cbe96f7a170aba7c7ef941f966d292.pdf)
3. Marcus Ritter, Alexander Geiß, Johannes Wehrstein, Alexandru Calotoiu, Thorsten Reimann, Torsten Hoefler, Felix Wolf: *Noise-Resilient Empirical Performance Modeling with Deep Neural Networks.* In Proc. of the 35th IEEE International Parallel and Distributed Processing Symposium (IPDPS), Portland, Oregon, USA, pages 23–34, IEEE, May 2021. [PDF](http://htor.inf.ethz.ch/publications/img/noiseresilientmodeling.pdf)
نیازمندی
| مقدار | نام |
|---|---|
| ~=5.13 | pyside2 |
| ~=1.18 | numpy |
| ~=3.2 | matplotlib |
| ~=4.47 | tqdm |
| ~=1.1 | pycubexr |
| ~=3.7 | marshmallow |
| ~=20.0 | packaging |
| ~=6.2 | pyobjc-framework-Cocoa |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl extrap-4.0.4:
pip install extrap-4.0.4.whl
نصب پکیج tar.gz extrap-4.0.4:
pip install extrap-4.0.4.tar.gz