Merge branch 'JournalSupercomputing23/24' of...

Merge branch 'JournalSupercomputing23/24' of http://lorca.act.uji.es/gitlab/martini/malleability_benchmark into JournalSupercomputing23/24

README.md

-# malleability_benchmark
+# Journal of Supercomputing Submission Branch
 
-Benchmark for a MPI malleable application
\ No newline at end of file
+## Overview
+This branch contains the codebase used for Proteo experiments and results presented for the paper in section "Paper Information" in the system Nasp. The code represents the state of the project at the time of submission and is tagged accordingly.
+
+## Paper Information
+- **Title:** Proteo: A Framework for the Generation and Evaluation of Malleable MPI Applications
+- **Authors:** Iker Martín-Álvarez, José I. Aliaga, Maribel Castillo, Sergio Iserte
+- **Journal:** Journal of Supercomputing
+- **Submission Date:** 30/11/2023
+
+## Branch Structure
+This branch is divided into the following 4 directories:
+- **Analysis**: Contains the scripts and notebook to perform analysis of Proteo executions.
+- **Codes**: Contains all the codes used to compile Proteo.
+- **Exec**: Contains the scripts to execute the CG and perform a post-mortem analysis.
+- **Results**: Contains the configuration files used to emulate the malleable emulation of the CG.
+
+## Installation
+
+### Prerequisites
+Before installing, ensure you have the following prerequisites:
+- MPI (MPICH) installed on your system. This code has been tested with MPICH versions 3.4.1 and 4.0.3 with the OFI netmod.
+- Slurm is installed on your system. This code has been tested with slurm-wlm 19.05.5.
+The following requisites are optional and only needed to process and analyse the data:
+- Python 3(Optional). Only if you want to perform the post-mortem processing or analyse the data.
+- Numpy 1.24.3(Optional). Only if you want to perform the post-mortem processing or analyse the data.
+- Pandas 1.5.3(Optional). Only if you want to perform the post-mortem processing or analyse the data.
+- Seaborn 0.12.2(Optional). Only if you want to analyse the data.
+- Matplotlib 3.7.1(Optional). Only if you want to analyse the data.
+- Scipy 1.10.1(Optional). Only if you want to analyse the data.
+- scikit-posthocs 0.7.0(Optional). Only if you want to analyse the data.
+
+
+### Steps
+1. Clone the repository to your local machine:
+
+    ```bash
+    $ git clone http://lorca.act.uji.es/gitlab/martini/malleability_benchmark.git
+    $ cd malleability_benchmark
+    $ git checkout JournalSupercomputing23/24
+    ```
+
+2. Compile the code using the `make` command:
+
+    ```bash
+    $ cd Codes/
+    $ make install_slurm
+    ```
+
+    This command compiles the code using the MPI (MPICH) library.
+
+3. Test the installation:
+    ```bash
+    $ cd ../Results
+    $ bash ../Exec/singleRun.sh ../Codes/test.ini
+    ```
+    This test launches an Slurm Job with a basic configuration file that performs a reconfiguration from 10 to 2 processes.
+    As soon as it ends, 4 files will appear, one is the slurm output, and the other 3 are Proteo's output. 
+    Example of a successful run with expected output:
+
+    ```bash
+    $ cat 
+    FIXME FILL ME
+    ```
+
+    FIXME Description to add
+### Clean Up
+To clean the installation and remove compiled binaries, use:
+
+```bash
+$ make clean
+```
+
+## Reproducing Experiments
+All the needed files to emulate the CG in Nasp are already in this branch. Keep in mind these only work properly in the system Nasp, as they have been modelled for that system.
+To reproduce the experiments performed with Proteo the following steps have to be performed:
+
+1. From the main directory of this branch execute:
+    ```bash
+    $ cd Results/DataRedist/Synch
+    $ bash ../Exec/runOFI.sh 5 > runOfi.txt
+    ```
+
+3. (Optional) When the experiments end, you can process the data. To perform this task the optional installation requisites must be meet. To process the data:
+    ```bash
+    $ cd Exec/
+    $ python3 MallTimes.py 
+    FIXME Añadir línea
+    $ python3 CreateResizeDataframe.py dataG.pkl dataM
+    $ python3 CreateIterDataframe.py dataG.pkl dataL
+    ```
+    After this commands, you will have two files called dataG.pkl, dataM.pkl and dataL*.pkl. These files can be opened in Pandas as dataframes to analyse the data.
+
+<!-- Terminar con paso 5 -->