Heterogenous computing for the local reconstruction algorithms of the CMS calorimeters

The increasing LHC luminosity in Run III and, consequently, the increased number of simultaneous proton-proton collisions (pile-up) pose significant challenges for the CMS experiment. These challenges will affect not only the data taking conditions, but also the data processing environment of CMS, which requires an improvement in the online triggering system to match the required detector performance. In order to mitigate the increasing collision rates and complexity of a single event, various approaches are being investigated. Heterogenous computing resources, recently becoming prominent and abundant, may be significantly better performing for certain types of workflows. In this work, we investigate implementations of common algorithms targeting heterogenous platforms, such as GPUs and FPGAs. The local reconstruction algorithms of the CMS calorimeters, given their granularity and intrinsic parallelizability, are among the first candidates considered for implementation in such heterogenous platforms. We will present the current development status and preliminary performance results. Challenges and various obstacles related to each platform, together with the integration into CMS experiments framework, will be further discussed.