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Cons: non trivial augmenting capabilities through additional C++ code.

Fast Feedback

This method of analysis reads the detector data from a fast storage layer called fast feedback (FFB), dedicated to the running experiment, and runs a prepared analysis. Any framework which can read XTC files can be adopted to run as a fast feedback application. The most common framework used with this method is psana.

Pros: trivial to setup; independent of the DAQ; ability to run the same code online and offline.

Cons: not quite real time (<5 minutes); may compete with the HDF5 translator if the translation services are run on the FFB storage layer.

Shared Memory (Experts only!)

The LCLS data acquisition system can allocate processes, called called shared memory servers, which receive the detector data through the network, build these contributions into events, and then hand over these events to one or more clients running on the same monitoring machine. The clients can be written in any language that can access shared memory. The most common frameworks adopted in LCLS as shared memory clients are are CASS, OnDA and  and psana. 

Pros: excellent  excellent real time capabilities (< 1s); ability to run the same code online and offline.

Cons: competes  if not using pre-existing software, requires very large time investment from both LCLS and the user.  competes with AMI resources (monitoring nodes); requires setting up the shared memory servers on the DAQ side.

Fast Feedback

This method of analysis reads the detector data from a fast storage layer called fast feedback (FFB), dedicated to the running experiment, and runs a prepared analysis. Any framework which can read XTC files can be adopted to run as a fast feedback application. The most common framework used with this method is psana.

Pros: trivial to setup; independent of the DAQ; ability to run the same code online and offline.

Cons: not quite real time (<5 minutes); may compete with the HDF5 translator if the translation services are run on the FFB storage layer.