From a8388fc30c27cf605d425f6ebbeceed6e140327e Mon Sep 17 00:00:00 2001 From: Alexey Rybalchenko Date: Thu, 9 Dec 2021 11:18:56 +0100 Subject: [PATCH] Update readme.md --- readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/readme.md b/readme.md index 8f5cabd2..c87910e8 100644 --- a/readme.md +++ b/readme.md @@ -1,6 +1,6 @@ ## 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 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: 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. The [FairMQ C++ library](https://github.com/FairRootGroup/FairMQ/) is designed to aid the implementation of such large-scale online data processing workflows by