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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: thausands 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 run offline (e.g: calibration, track finding, etc) have to now run 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: thousands 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 run offline (e.g: calibration, track finding, etc) have to now 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