Mohr H., Dritschler T., Ardila L.E., Balzer M., Caselle M., Chilingaryan S., Kopmann A., Rota L., Schuh T., Vogelgesang M., Weber M.
in Journal of Instrumentation, 12 (2017), C04019. DOI:10.1088/1748-0221/12/04/C04019
© 2017 IOP Publishing Ltd and Sissa Medialab srl. In this work, we investigate the use of GPUs as a way of realizing a low-latency, high-throughput track trigger, using CMS as a showcase example. The CMS detector at the Large Hadron Collider (LHC) will undergo a major upgrade after the long shutdown from 2024 to 2026 when it will enter the high luminosity era. During this upgrade, the silicon tracker will have to be completely replaced. In the High Luminosity operation mode, luminosities of 5-7 × 1034 cm-2s-1 and pileups averaging at 140 events, with a maximum of up to 200 events, will be reached. These changes will require a major update of the triggering system. The demonstrated systems rely on dedicated hardware such as associative memory ASICs and FPGAs. We investigate the use of GPUs as an alternative way of realizing the requirements of the L1 track trigger. To this end we implemeted a Hough transformation track finding step on GPUs and established a low-latency RDMA connection using the PCIe bus. To showcase the benefits of floating point operations, made possible by the use of GPUs, we present a modified algorithm. It uses hexagonal bins for the parameter space and leads to a more truthful representation of the possible track parameters of the individual hits in Hough space. This leads to fewer duplicate candidates and reduces fake track candidates compared to the regular approach. With data-transfer latencies of 2 μs and processing times for the Hough transformation as low as 3.6 μs, we can show that latencies are not as critical as expected. However, computing throughput proves to be challenging due to hardware limitations.
Master Thesis, Faculty for Physics, Karlsruhe Institute of Technology, 2016.
In this work we present an evaluation of GPUs as a possible L1 Track Trigger for the High Luminosity LHC, effective after Long Shutdown 3 around 2025.
The novelty lies in presenting an implementation based on calculations done entirely in software, in contrast to currently discussed solutions relying on specialized hardware, such as FPGAs and ASICs.
Our solution relies on using GPUs for the calculation instead, offering floating point calculations as well as flexibility and adaptability. Normally the involved data transfer latencies make GPUs unfeasible for use in low latency environments. To this end we use a data transfer scheme based on RDMA technology. This mitigates the normally involved overheads.
We based our efforts on previous work by the collaboration of the KIT and the English track trigger group [An FPGA-based track finder for the L1 trigger of the CMS experiment at the high luminosity LHC] whose algorithm was implemented on FPGAs.
In addition to the Hough transformation used regularly, we present our own version of the algorithm based on a hexagonal layout of the binned parameter space. With comparable computational latency and workload, the approach produces significantly less fake track candidates than the traditionally used method. This comes at a cost of efficiency of around 1 percent.
This work focuses on the track finding part of the proposed L1 Track Trigger and only looks at the result of a least squares fit to make an estimate of the performance of said seeding step. We furthermore present our results in terms of overall latency of this novel approach.
While not yet competitive, our implementation has surpassed initial expectations and are on the same order of magnitude as the FPGA approach in terms of latencies. Some caveats apply at the moment. Ultimately, more recent technology, not yet available to us in the current discussion will have to be tested and benchmarked to come to a more complete assessment of the feasibility of GPUs as a means of track triggering
at the High-Luminosity-LHC’s CMS experiment.
First assessor: Prof. Dr. Marc Weber
Second assessor: Prof. Dr. Ulrich Husemann
Supervised by Dipl.-Inform. Timo Dritschler