TPC data compression

In the collisions of ultra-relativistic heavy ions in fixed-target and collider experiments, multiplicities of several ten thousand charged particles are generated. The main devices for tracking and particle identification are large-volume tracking detectors (TPCs) producing raw event sizes in excess of 100 Mbytes per event. With increasing data rates, storage becomes the main limiting factor in such experiments and, therefore, it is essential to represent the data in a way that is as concise as possible. In this paper, we present several compression schemes, such as entropy encoding, modified vector quantization, and data modeling techniques applied on real data from the CERN SPS experiment NA49 and on simulated data from the future CERN LHC experiment ALICE

Level-3 trigger for a heavy ion experiment at LHC

At the upcoming large hadron collider (LHC) at CERN one expects to measure 20,000 particles in a single Pb-Pb event resulting in a data rate of ~75 MByte/event. The event rate is limited by the bandwidth of the storage system. Higher rates are possible by selecting interesting events and sub-events (Level-3 trigger) or compressing the data efficiently with modeling techniques. Both techniques require a fast parallel pattern recognition. One possible solution to process the detector data at such rates is a farm of clustered SMP nodes, based on off-the-shelf PCs, and connected by a high bandwidth, low latency network. (8 refs)

High-level trigger system for the LHC ALICE experiment

The central detectors of the ALICE experiment at LHC will produce a data size of up to 75 MB/event at an event rate less than approximately equals 200 Hz resulting in a data rate of similar to 15 GB/s. Online processing of the data is necessary in order to select interesting (sub)events ("High Level Trigger"), or to compress data efficiently by modeling techniques. Processing this data requires a massive parallel computing system (High Level Trigger System). The system will consist of a farm of clustered SMP-nodes based on off- the-shelf PCs connected with a high bandwidth low latency network

Online pattern recognition for the ALICE high level trigger

The ALICE High Level Trigger system needs to reconstruct events online at high data rates. Focusing on the Time Projection Chamber we present two pattern recognition methods under investigation: the sequential approach (cluster finding, track follower) and the iterative approach (Hough Transform, cluster assignment, re-fitting). The implementation of the former in hardware indicates that we can reach the designed inspection rate for p-p collisions of 1 kHz with 98% efficiency

Scaling the CERN OpenStack cloud

CERN has been running a production OpenStack cloud since July 2013 to support physics computing and infrastructure services for the site. In the past year, CERN Cloud Infrastructure has seen a constant increase in nodes, virtual machines, users and projects. This paper will present what has been done in order to make the CERN cloud infrastructure scale out