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Co-authored-by: Dennis Klein <d.klein@gsi.de>
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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). The expected data rates require a non-trivial amount of high performance compute (HPC) resources, i.e: Thausends of CPU/GPU cores per experiment. Online data processing/compression are crucial to fulfill the storage capacity requirements. The complexity of tasks that need to be performed during the online (synchronous) data processing is significantly higher than ever before. Complex tasks that usually runs offline (e.g: Calibration, Track finding, etc) have to run now online in a high performance and high throughput environment.
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). The expected data rates require a non-trivial amount of high performance compute (HPC) resources, i.e: Thausends of CPU/GPU cores per experiment. Online data processing/compression are crucial to stay within storage capacity limits. The complexity of tasks that need to be performed during the online (synchronous) data processing is significantly higher than ever before. Complex tasks that usually runs offline (e.g: Calibration, Track finding, etc) have to run now 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