Yeo, B. ; Kusiak, K. A. ; Leggett, C. ; Mania, G. ; Niermann, J. ; Styles, N.DESY* ; Swatman, S. N. ; Salzburger, A. ; GRASLAND, H.Extern* ; Gessinger, P.Extern* ; Gray, H. ; Joube, S. ; Krasznahorkay, A.

2022

2022Connecting the Dots, Princeton, New JerseyPrinceton, New Jersey, USA, 31 May 2022 - 2 Jun 20222022-05-312022-06-02 [10.5281/ZENODO.8119504]  

Abstract: In the future HEP experiments, there will be a significant increase in computing power required for track reconstruction due to the large data size. As track reconstruction is inherently parallelizable, heterogeneous computing with GPU hardware is expected to outperform the conventional CPUs. To achieve better maintainability and high quality of track reconstruction, a host-device compatible event data model and tracking geometry are necessary. However, such a flexible design can be challenging because many GPU APIs restrict the usage of modern C++ features and also have a complicated user interface. To overcome those issues, the ACTS community has launched several R&D projects: traccc as a GPU track reconstruction demonstrator, detray as a GPU geometry builder, and vecmem as a GPU memory management tool. The event data model of traccc is designed using the vecmem library, which provides an easy user interface to host and device memory allocation through C++ standard containers. For a realistic detector design, traccc utilizes the detray library which applies compile-time polymorphism in its detector description. A detray detector can be shared between the host and the device, as the detector subcomponents are serialized in a vecmem-based container. Within traccc, tracking algorithms including hit clusterization and seed finding have been ported to multiple GPU APIs. In this presentation, we highlight the recent progress in traccc and present benchmarking results of the tracking algorithms.

1. HIDSS-0002 - DASHH: Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter (2019_IVF-HIDSS-0002) (2019_IVF-HIDSS-0002)