GravWave-AppleM2-Analysis

Overview

The Apple Silicon Gravitational-Wave Cluster project focuses on harnessing the computational power of Apple Silicon M2 processors to build a high-performance cluster dedicated to gravitational-wave astronomy. Gravitational waves, predicted by Einstein's theory of general relativity, offer a unique window into the cosmos. This project aims to leverage the efficiency and capabilities of Apple Silicon to create a scalable and efficient computing environment for processing and analyzing gravitational-wave data.

Key Objectives

Performance Optimization: Utilizing Apple Silicon M2 processors to enhance computing speed and efficiency in gravitational-wave data analysis.
Cluster Scalability: Building a scalable cluster architecture that can handle the complexities of processing large datasets inherent in gravitational-wave astronomy.

PyCBC Project

Environment Setup for Contributing

To contribute to PyCBC and work with the example pycbc_inspiral script, follow these steps to set up your development environment:

1. Fork the PyCBC Repository

Before proceeding, fork the PyCBC repository by following the instructions in the CONTRIBUTING.md file.

Make a fork of PyCBC

Go to the PyCBC repository home page
Click on the Fork button (top-right-hand corner)
Select the namespace that you want to create the fork in, this will usually be your personal namespace

2. Clone Your Forked Repository

Clone your forked PyCBC repository onto your local machine. Open a terminal and navigate to the directory where you want to clone the repository:

git clone https://github.com/<git-username>/pycbc.git
cd pycbc

3. Activate Conda Environment

Activate your conda environment or create a new one. If you use an existing environment where PyCBC is already installed, consider uninstalling it from that environment.

conda activate your-environment

# If using an existing environment where PyCBC is already installed:
pip uninstall pycbc

4. Install Dependencies

Install the necessary dependent packages by running the following commands:

pip install -r requirements.txt
pip install -r companion.txt

5. Install PyCBC from Source

Install PyCBC from the source code in your cloned repository:

pip install .

6. Test pycbc_inspiral

Navigate to the examples/inspiral directory:

cd examples/inspiral

In this directory, there is a script called run.sh. Execute it:

bash run.sh

This setup completes the installation of PyCBC from the source code, and you can now test the pycbc_inspiral script as instructed.

Installing lalsuite-extra

To obtain the necessary data files for your analysis, follow these steps:

1. Install lalsuite

conda install --channel conda-forge lalsuite

2. Clone the repository

git clone https://git.ligo.org/lscsoft/lalsuite.git

3. Get additional data files from lalsuite-extra

Navigate to the lalsuite directory:

cd lalsuite

Clone the lalsuite-extra repository:

git clone https://git.ligo.org/lscsoft/lalsuite-extra.git

Change directory to lalsuite-extra:

cd lalsuite-extra

Run the following commands to configure and install:

./00boot
./configure --prefix=${HOME}/<path_to_lalsuite-extra>
make install

Set the LAL_DATA_PATH environment variable:

echo 'export LAL_DATA_PATH=${HOME}/<path_to_lalsuite-extra>/share/lalsimulation' >> ~/.zshrc

or, if the above command is not effective:

echo 'export LAL_DATA_PATH=${HOME}/<path_to_lalsuite-extra>/share/lalsimulation' >> ~/.bashprofile

HTCondor Installation Guide for MacOS

To utilize HTCondor as a user on MacOS, follow the steps below for a seamless installation:

1. Download HTCondor

Open a new terminal and download HTCondor:

cd
curl -fsSL https://get.htcondor.org | /bin/bash -s -- --download

2. Unpack and Rename

Unpack the downloaded tarball and rename the resulting directory:

tar -x -f condor.tar.gz
mv condor-*stripped condor

3. Navigate to the Condor Directory

Navigate to the condor directory, created in the previous step:

cd condor

4. Installation and Configuration

Execute the following command for installation and configuration:

./bin/make-personal-from-tarball

5. Activate HTCondor Environment

Whenever you want to use HTCondor, execute the following command:

. ~/condor/condor.sh

6. Start HTCondor

To start HTCondor, especially after a machine reboot:

condor_master

7. Verify Installation

You can verify the HTCondor installation by executing the following commands:

condor_status: Displays the status and information about the computational
condor_q: Displays information about the jobs submitted to the HTCondor system.

8. Useful HTCondor Commands:

condor_q -nobatch: To get job IDs
condor_q -analyze JOBID: To analyze a job:
condor_release JOBID: To remove a job from HOLD state
condor_rm JOBID: To remove a job from IDLE state

Setting up Pegasus and Resolving Installation Issues

Installing Pegasus

After installing PyCBC, if you encounter any errors while executing Pegasus workflow, please consider doing the following steps to resolve issues

a. Install Pycbc (If not done previously)

pip install .

Install homebrew before as mention here: homebrew installation and follow the steps below

Note: To generate the executable workflow

b. Tap into Pegasus-tools using

brew tap pegasus-isi/tools

c. Install Pegasus

Now, install Pegasus using Homebrew:

brew install pegasus

These steps are essential to overcome any errors encountered during the installation process and to ensure a successful setup.

neeresh / GravWave-AppleM2-Analysis