Speaker
Description
The Large Hadron Collider (LHC) will be upgraded to High-luminosity LHC, increasing the number of simultaneous proton-proton collisions (pile-up, PU) by several-folds. The harsher PU conditions lead to exponentially increasing combinatorics in charged-particle tracking, placing a large demand on the computing resources. The projection on required computing resources exceeds the computing budget with the current algorithms running on single-thread CPUs. Motivated by the rise of heterogeneous computing in high-performance computing centers, we present Line Segment Tracking (LST), a highly parallelizeable algorithm that can run efficiently on GPUs and has been integrated to the CMS experiment central software. The usage of Alpaka framework for the algorithm implementation allows better portability of the code to run on different types of commercial parallel processors allowing flexibility on which processors to purchase for the experiment in the future. To verify a similar computational performance with a native solution, the alpaka implementation is compared with a cuda one on a NVIDIA Tesla V100 GPU. The algorithm creates short track segments in parallel, and progressively form higher level objects by linking segments that are consistent with genuine physics track hypothesis. The computing and physics performance are on par with the latest, multi-CPU versions of existing CMS tracking algorithms.
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