GPU-Accelerated Particle-in-Cell Code on Minsky

Herten, Andreas; Brömmel, Dirk; Pleiter, D.

doi:10.1007/978-3-319-67630-2_17

Cited by 1 publication

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of cylindrical straw tube detectors, several track reconstruction approaches have been developed over the past years (e.g. [9,10,14,17,18,[28][29][30]). In this work, we compare lotf to two of these approaches: the track reconstruction technique from [14] (referred to as Babai+20) and the BarrelTrackFinder algorithm [30] implemented in Pan-daRoot [22].…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Additionally, a GPU-based (e.g. as in [18,28]) or FPGA-based implementation [17] could help to further increase the average reconstruction rate to meet the requirements for the PANDA in-situ track reconstruction. The latter approach is particularly promising as the authors report an improvement of up to 3 orders of magnitude compared to classical CPU-based approaches.…”

Section: Speed Of the Algorithmmentioning

confidence: 99%

“…These methods build upon techniques such as the Hough transformation [8,9], the Recursive Annealing Fit [10], the cellular automaton with Riemann mapping [11], the triplet finder [12], attributespace-connected morphological filters [13,14], or deeplearning [15,16]. Additionally, a significant effort focused on parallelizing these algorithms using e.g., FPGAs [17] or GPUs [8,18]. Finally, some algorithms focused on supplementing track finders, e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reconstructing charged-particle trajectories in the PANDA Straw Tube Tracker using the LOcal Track Finder (LOTF) algorithm

et al. 2023

View full text Add to dashboard Cite

We present the LOcal Track Finder (lotf) algorithm, a method that performs charged-particle trajectory reconstruction using the Straw Tube Tracker, one of the central trackers of the antiProton ANnihilation at DArmstadt (PANDA) detector. The algorithm builds upon the neighboring relations of the tubes to connect individual hits and form track candidates. In addition, it uses a local fitting procedure to handle regions where several tracks overlap and utilizes a system of virtual nodes to reconstruct the z-information of the particle trajectories. We generated 30,000 events to assess the performance of our approach and compared its global track assignment efficiency with respect to two other track reconstruction methods. lotf has (1) an average of 85% of found tracks, (2) the largest number of Fully Pure tracks, (3) the lowest amount of incorrect reconstructions, and ( 4) is significantly faster than the other two approaches. Further, we compared the z-reconstruction performance with one of the two alternative methods and show that lotf improves the median z-error by a factor of 8.7. Finally, we tested our method using 3750 data sets composed of 4 events each, demonstrating that our approach handles cases in which events are mixed. The raw (without parallelization) average reconstruction rate is about 68,000 hits/s, which makes the present algorithm promising for online data selection and processing.

show abstract