  The CMS endcap calorimeter upgrade for the High Luminosity LHC in 2027 uses
silicon sensors to achieve radiation tolerance, with the further benefit of a
very high readout granularity. Small scintillator tiles with individual SiPM
readout are used in regions permitted by the radiation levels. A reconstruction
framework is being developed to fully exploit the granularity and other
significant features of the detector like precision timing, especially in the
high pileup environment of HL-LHC. An iterative clustering framework (TICL) has
been put in place, and is being actively developed. The framework takes as
input the clusters of energy deposited in individual calorimeter layers
delivered by the CLUE algorithm, which has recently been revised and tuned.
Mindful of the projected extreme pressure on computing capacity in the HL-LHC
era, the algorithms are being designed with modern parallel architectures in
mind. Important speedup has recently been obtained for the clustering algorithm
by running it on GPUs. Machine learning techniques are being developed and
integrated into the reconstruction framework. This paper will describe the
approaches being considered and show first results.
