  The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) will
produce particle collisions with up to 200 simultaneous proton-proton
interactions. These unprecedented conditions will create a combinatorial
complexity for charged-particle track reconstruction that demands a
computational cost that is expected to surpass the projected computing budget
using conventional CPUs. Motivated by this and taking into account the
prevalence of heterogeneous computing in cutting-edge High Performance
Computing centers, we propose an efficient, fast and highly parallelizable
bottom-up approach to track reconstruction for the HL-LHC, along with an
associated implementation on GPUs, in the context of the Phase 2 CMS outer
tracker. Our algorithm, called Segment Linking (or Line Segment Tracking),
takes advantage of localized track stub creation, combining individual stubs to
progressively form higher level objects that are subject to kinematical and
geometrical requirements compatible with genuine physics tracks. The local
nature of the algorithm makes it ideal for parallelization under the Single
Instruction, Multiple Data paradigm, as hundreds of objects can be built
simultaneously. The computing and physics performance of the algorithm has been
tested on an NVIDIA Tesla V100 GPU, already yielding efficiency and timing
measurements that are on par with the latest, multi-CPU versions of existing
CMS tracking algorithms.
