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## FairMQ in a nutshell ## 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). High detector data readout rates alone already require a non-trivial amount of distributed compute resources in the order of hundreds of servers per experiment. To achieve sufficient data compression rates in order to stay within the limits of economically feasible data storage capacity requirements, the complexity of tasks that need to be performed during the synchronous (online) data processing is significantly increased as well compared to previous generations of experiments. 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.
The [FairMQ C++ library](https://github.com/FairRootGroup/FairMQ/) is designed to aid the implementation of such large-scale online data processing workflows by 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), - providing an **asynchronous message passing abstraction** that integrates different existing data transport technologies (no need to re-invent the wheel),
* being **data format agnostic** (suitable data formats are usually experiment-specific), and - 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),
* providing other **basic building blocks** such as a simple state machine based execution framework and a plugin mechanism to integrate with external config/control systems. - being **data format agnostic** (suitable data formats are usually experiment-specific), and
- providing other **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 experiment software experts to implement higher-level experiment-specific applications. FairMQ is not an end-user application, but a library and framework used by experiment software experts to implement higher-level experiment-specific applications.