Clustering in the Heterogeneous Reconstruction Chain of the CMS HGCAL Detector

Abstract

Abstract We present an important milestone for the CMS High Granularity Calorimeter (HGCAL) event reconstruction: the deployment of the GPU clustering algorithm (CLUE) to the CMS software. The connection between GPU CLUE and the preceding GPU calibration step is thus made possible, further extending the heterogeneous chain of HGCAL’s reconstruction framework. In addition to improvements brought by CLUE’s deployment, new recursive device kernels are added to efficiently calculate the position and energy of CLUE clusters. Data conversions between GPU and CPU are included to facilitate the validation of the algorithms and increase the flexibility of the reconstruction. For the first time in HGCAL, conditions data are deployed to the GPU and made available on demand at any stage of the heterogeneous reconstruction. This is achieved via a new geometry ordering scheme in which physical and memory locations are connected. This scheme is successfully tested with the GPU CLUE version reported here, and is expected to have a broad range of applicability for future heterogeneous developments in CMS. Finally, the performance of the combined calibration and clustering algorithms on GPU is assessed and compared to its CPU counterpart.