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
Diploma Thesis, Faculty for Computer Science, Karlsruhe Institute of Technology, 2015.
NVIDIAs recently presented GPUDirect RDMA technology allows direct communication on the PCIe bus between NVIDIA GPUs and other devices. The ability to bypass main memory and write and read directly into/from the GPU memory is expected to decrease the latency of data tranfer actions. KIRO (KITs InfiniBand remote communication library) is used to provide high-performance communication for control systems at the image beam line of the ANKA synchrotron. To improve the reaction time of control systems and be ready for cameras with throughput of several gigabytes per second, we have modified KIRO to use the GPUDirect RDMA technology. Using this approach we were able to reach throughput rates of 40 GBit/s and could nearly halve the latency. The GPUDirect technology and the updated architecture of KIRO will be presented in this work. The achieved performance and feasability of the integration in the current workflow will be discussed.
First assessor: Prof. Dr. Wolfgang Karl
Second assessor: Prof. Dr. Marc Weber
Supervised by Dipl.-Inform. Timo Dritschler, Dr. Ing. Mario Kicherer
Master thesis, Faculty of Computer Science, Karlsruhe Institute of Technology, 2015.
An ever increasing number of large tomographic images is recorded at synchrotron facilities world wide. Due to the drastic increase of data volumes, there is a recent trend to provide data analysis services at the facilities as well. The ASTOR project aims to realize a cloud-based infrastructure for remote data analysis and visualization of tomographic data. A key component is a web-based data browser to select data sets and request a virtual machine for analysis of this data. One of the challenges is to provide a fast preview of 3D volumes but also 3D sequences. Since a standard data sets exceed 10 gigabytes, standard visualization techniques are not feasible and new data reduction techniques have to be developed.
First assessor: Prof. Dr.-Ing. Carsten Dachsbacher
Second assessor: Dr. Suren Chilingaryan
Supervised by Dr. Andreas Kopmann