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Register a new userLHCb trigger streams optimization
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Added by
Nikita Kazeev
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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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. We present a method for finding an optimal stream composition. The method is applied to a part of the LHCb data (Turbo stream) on the stage where it is prepared for user physics analysis. This results in an expected improvement of 15% in the speed of user analysis jobs, and will be applied on data to be recorded in 2017.
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The main b-physics trigger algorithm used by the LHCb experiment is the so-called topological trigger. The topological trigger selects vertices which are a) detached from the primary proton-proton collision and b) compatible with coming from the decay of a b-hadron. In the LHC Run 1, this trigger, which utilized a custom boosted decision tree algorithm, selected a nearly 100% pure sample of b-hadrons with a typical efficiency of 60-70%; its output was used in about 60% of LHCb papers. This talk presents studies carried out to optimize the topological trigger for LHC Run 2. In particular, we have carried out a detailed comparison of various machine learning classifier algorithms, e.g., AdaBoost, MatrixNet and neural networks. The topological trigger algorithm is designed to select all interesting decays of b-hadrons, but cannot be trained on every such decay. Studies have therefore been performed to determine how to optimize the performance of the classification algorithm on decays not used in the training. Methods studied include cascading, ensembling and blending techniques. Furthermore, novel boosting techniques have been implemented that will help reduce systematic uncertainties in Run 2 measurements. We demonstrate that the reoptimized topological trigger is expected to significantly improve on the Run 1 performance for a wide range of b-hadron decays.
The LHCb experiment will operate at a luminosity of $2times10^{33}$ cm$^{-2}$s$^{-1}$ during LHC Run 3. At this rate the present readout and hardware Level-0 trigger become a limitation, especially for fully hadronic final states. In order to maintain a high signal efficiency the upgraded LHCb detector will deploy two novel concepts: a triggerless readout and a full software trigger.
Hundreds of millions of network cameras have been installed throughout the world. Each is capable of providing a vast amount of real-time data. Analyzing the massive data generated by these cameras requires significant computational resources and the demands may vary over time. Cloud computing shows the most promise to provide the needed resources on demand. In this article, we investigate how to allocate cloud resources when analyzing real-time data streams from network cameras. A resource manager considers many factors that affect its decisions, including the types of analysis, the number of data streams, and the locations of the cameras. The manager then selects the most cost-efficient types of cloud instances (e.g. CPU vs. GPGPU) to meet the computational demands for analyzing streams. We evaluate the effectiveness of our approach using Amazon Web Services. Experiments demonstrate more than 50% cost reduction for real workloads.
This paper presents the design of the LHCb trigger and its performance on data taken at the LHC in 2011. A principal goal of LHCb is to perform flavour physics measurements, and the trigger is designed to distinguish charm and beauty decays from the light quark background. Using a combination of lepton identification and measurements of the particles transverse momenta the trigger selects particles originating from charm and beauty hadrons, which typically fly a finite distance before decaying. The trigger reduces the roughly 11,MHz of bunch-bunch crossings that contain at least one inelastic $pp$ interaction to 3,kHz. This reduction takes place in two stages; the first stage is implemented in hardware and the second stage is a software application that runs on a large computer farm. A data-driven method is used to evaluate the performance of the trigger on several charm and beauty decay modes.
A very compact architecture has been developed for the first level Muon Trigger of the LHCb experiment that processes 40 millions of proton-proton collisions per second. For each collision, it receives 3.2 kBytes of data and it finds straight tracks within a 1.2 microseconds latency. The trigger implementation is massively parallel, pipelined and fully synchronous with the LHC clock. It relies on 248 high density Field Programable Gate arrays and on the massive use of multigigabit serial link transceivers embedded inside FPGAs.
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