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layout: spotlight

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# The name of the software
name: FairMQ

# The date when the software was added to the spotlights YYYY-MM-DD
date_added: 2022-04-01

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preview_image: fairmq/FAIR_Logo.svg

# One or two sentences describing the software
excerpt: FairMQ is designed to help implement large-scale data processing workflows needed in next-generation particle physics experiments.

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# Title at the top, inside the title-content-container
title: FairMQ - A message queuing library and framework

# Add at least one keyword
keywords:
    - framework
    - workflow library

# The Helmholtz research field
hgf_research_field: Matter

# At least one responsible centre
hgf_centers:
    - Helmholtz Centre for Heavy Ion Research (GSI)

# List of other contributing organisations (optional)
contributing_organisations:
    - name: European Organization for Nuclear Research (CERN)

# List of scientific communities
scientific_community:
    - Particle physics

# Impact on community (optional, not implemented yet)
impact_on_community: ESCAPE 2020

# An e-mail address
contact: D.Klein@gsi.de

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platforms:
    - type: github
      link_as: https://github.com/FairRootGroup/FairMQ

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license: LGPL-3.0-only

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costs: free

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software_type:
    - Data Analytics

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application_type:
    - Desktop

# List of programming languages (optional)
programming_languages:
    - C++

# DOI (without URL, just 10.1000/1.0000000 ) (optional)
doi: 10.5281/zenodo.1689985

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# FairMQ in a nutshell

Next-generation Particle Physics Experiments at [GSI](https://www.gsi.de)/[FAIR](https://www.gsi.de/forschungbeschleuniger/fair) and [CERN](https://home.web.cern.ch/) are facing [unprecedented data processing challenges](https://doi.org/10.1051/epjconf/201921405010). Expected data rates require a non-trivial amount of high performance compute (HPC) resources in the order of thousands of CPU/GPU cores per experiment. Online (synchronous) data processing (compression) is crucial to stay within storage capacity limits. The complexity of tasks that need to be performed during the online data processing is significantly higher than ever before. Classically, complex tasks like calibration and track finding run in an offline (asynchronous) environment. Now they have to run online in a high performance and high throughput environment.

The [FairMQ C++ library](https://github.com/FairRootGroup/FairMQ/) is designed to aid the implementation of such large-scale online data processing workflows by

- providing an **asynchronous message passing abstraction** that integrates different existing data transport technologies (no need to re-invent the wheel),
- providing a **reasonably efficient data transport service** (zero-copy, high throughput - [TCP](https://zeromq.org/), [SHMEM](https://doi.org/10.1051/epjconf/201921405029) and [RDMA](https://doi.org/10.1051/epjconf/201921405022) implementations available),
- being **data format agnostic** (suitable data formats are usually experiment-specific), and
- providing further **basic building blocks** such as a simple state machine-based execution framework and a plugin mechanism to integrate with external config/control systems.

FairMQ is not an end-user application, but a library and framework used by software experts to implement higher-level experiment-specific applications.

<div class="spotlights-text-image">
<img src="{{ site.directory.images | relative_url}}spotlights/fairmq/AliceO2DebugGUIScreenshotEPN.png" alt="Screenshot of AliceO2 Debug GUI showing the data processing workflow of a single event processing node">
<span>Screenshot of AliceO2 Debug GUI showing the data processing workflow of a single event processing node.</span>
</div>

The screenshot shows a visualization of the data processing workflow on a single [Alice](https://home.cern/science/experiments/alice) event processing node (The "O2 Framework debug GUI" tool in the screenshot is part of the [AliceO2 project](https://aliceo2group.github.io/AliceO2/)). Data logically flows along the yellow edges (in this case via the FairMQ shmem data transport) through the various processing stages of which some are implemented as GPU and others as CPU algorithms.

Initially designed with the online data processing in mind, FairMQ has been successfully used to parallelize offline [simulation](https://doi.org/10.1051/epjconf/201921402029) and [analysis](https://doi.org/10.1051/epjconf/201921405045) workloads as well.