Refine
Document Type
- Doctoral Thesis (2)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- HLT (2) (remove)
Institute
- Informatik und Mathematik (1)
- Physik (1)
A new era in experimental nuclear physics has begun with the start-up of the Large Hadron Collider at CERN and its dedicated heavy-ion detector system ALICE. Measuring the highest energy density ever produced in nucleus-nucleus collisions, the detector has been designed to study the properties of the created hot and dense medium, assumed to be a Quark-Gluon Plasma.
Comprised of 18 high granularity sub-detectors, ALICE delivers data from a few million electronic channels of proton-proton and heavy-ion collisions.
The produced data volume can reach up to 26 GByte/s for central Pb–Pb
collisions at design luminosity of L = 1027 cm−2 s−1 , challenging not only the data storage, but also the physics analysis. A High-Level Trigger (HLT) has been built and commissioned to reduce that amount of data to a storable value prior to archiving with the means of data filtering and compression without the loss of physics information. Implemented as a large high performance compute cluster, the HLT is able to perform a full reconstruction of all events at the time of data-taking, which allows to trigger, based on the information of a complete event. Rare physics probes, with high transverse momentum, can be identified and selected to enhance the overall physics reach of the experiment.
The commissioning of the HLT is at the center of this thesis. Being deeply embedded in the ALICE data path and, therefore, interfacing all other ALICE subsystems, this commissioning imposed not only a major challenge, but also a massive coordination effort, which was completed with the first proton-proton collisions reconstructed by the HLT. Furthermore, this thesis is completed with the study and implementation of on-line high transverse momentum triggers.
The ALICE High-Level-Trigger (HLT) is a large scale computing farm designed and constructed for the purpose of the realtime reconstruction of particle interactions (events) inside the ALICE detector. The reconstruction of such events is based on the raw data produced in collisions inside the ALICE at the Large Hadron Collider. The online reconstruction in the HLT allows the triggering on certain event topologies and a significant data reduction by applying compression algorithms. Moreover, it enables a real-time verification of the quality of the data.
To receive the raw data from the various sub-detectors of ALICE, the HLT is equipped with 226 custom built FPGA-based PCI-X cards, the H-RORCs. The H-RORC interfaces the detector readout electronics to the nodes of the HLT farm. In addition to the transfer of raw data, 108 H-RORCs host 216 Fast-Cluster-Finder (FCF) processors for the Time-Projection-Chamber (TPC). The TPC is the main tracking detector of ALICE and contributes with up to 16 GB/s to over 90% of the overall data volume. The FCF processor implements the first of two steps in the data reconstruction of the TPC. It calculates the space points and their properties from charge clouds (clusters) created by charged particles traversing the TPCs gas volume. Those space points are not only the base for the tracking algorithm, but also allow for a Huffman-based data compression, which reduces the data volume by a factor of 4 to 6.
The FCF processor is designed to cope with any incoming data rate up to the maximum bandwidth of the incoming optical link (160 MB/s) without creating back-pressure to the detectors readout electronics. A performance comparison with the software implementation of the algorithm shows a speedup factor of about 20 compared with one AMD Opteron 6172 Core @ 2.1 GHz, the CPU type used in the HLT during the LHC Run1 campaign. Comparison with an Intel E5-2690 Core @ 3.0 GHz, the CPU type used by the HLT for the LHC Run2 campaign, results in a speedup factor of 8.5. In total numbers, the 216 FCF processors provide the computing performance of 4255 AMD Opteron cores or 2203 Intel cores of the previously mentioned type. The performance of the reconstruction with respect to the physics analysis is equivalent or better than the official ALICE Offline clusterizer. Therefore, ALICE data taking was switched in 2011 to FCF cluster recording and compression only, discarding the raw data from the TPC. Due to the capability to compress the clusters, the recorded data volume could be increased by a factor of 4 to 6.
For the LHC Run3 campaign, starting in 2020, the FCF builds the foundation of the ALICE data taking and processing strategy. The raw data volume (before processing) of the upgraded TPC will exceed 3 TB/s. As a consequence, online processing of the raw data and compression of the results before it enters the online computing farms is an essential and crucial part of the computing model.
Within the scope of this thesis, the H-RORC card and the FCF processor were developed and built from scratch. It covers the conceptual design, the optimisation and implementation, as well as the verification. It is completed by performance benchmarks and experiences from real data taking.