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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. Most complex tasks like calibration and track finding classically ran in an offline (asynchronous) environment and have to now run online in a high performance and high throughput environment under more strict resource constraints.
+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. Complex tasks like calibration and track finding classically ran in an offline (asynchronous) environment and 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