streams-optimization 1.0.0.dev2

A library for LHCb trigger/stripping streams optimization

The LHCb experiment stores around $10^{11}$ collision events per year. A typical physics analysis deals with a final sample of up to $10^7$ events. Event preselection algorithms (lines) are used for data reduction. Since the data are stored in a format that requires sequential access, the lines are grouped into several output file streams, in order to increase the efficiency of user analysis jobs that read these data. The scheme efficiency heavily depends on the stream composition. By putting similar lines together and balancing the stream sizes it is possible to reduce the overhead. This library implements a method for finding an optimal stream composition.

It takes an arbitary differentiable loss functon and produces a streaming scheme that’s optimized to that loss loss function. For Tesla streams we used $sum_{streams} E(N_{events}) E(N_{lines})$.

Please read more and cite the paper submitted to CHEP proceedings: https://arxiv.org/abs/1702.05262

## Installation As of time of writing (09.03.2017), PyPi doesn’t have the required versions of theano and lasagne. You should install them via: `bash pip install -r https://raw.githubusercontent.com/Lasagne/Lasagne/master/requirements.txt pip install https://github.com/Lasagne/Lasagne/archive/master.zip ` then proceed with installing the streams-optimization package: `bash python setup.py install `