Parallelizing and Optimizing LHCb-Kalman for Intel Xeon Phi KNL Processors

Abstract

Real time data processing is an important component of particle physics experiments with large computing resource requirements. As the Large Hadron Collider (LHC) at CERN is preparing for its next upgrade the LHCb experiment is upgrading its detector for a 30x increase in data throughput. In preparation for this upgrade the experiment is considering a number of architectural improvements encompassing both its software and hardware infrastructure. One of the hardware platforms under consideration is the Intel Xeon-Phi Knights Landing processor. Thanks to its on-package high-bandwidth memory and many-core architecture it offers an interesting alternative to more traditional server systems. We present a scalable, multi-threaded and NUMA-aware Kalman filter proto-application for particle track fitting expressed in terms of generic parallel patterns using the GrPPI interface. We show how code maintainability and readability improves, while maintaining comparable levels of performance to the baseline implementation. This is achieved by keeping the parallel algorithms in the underlying framework generic, but topology aware through the use of the Portable Hardware Locality (hwloc) library, which allows us to target different architectures with the same program. We measure the performance of our topology-aware GrPPI Kalman filter implementation on the Intel Xeon-Phi Knights Landing platform and conclude on the feasibility of integrating such high-level parallelization libraries in complex software frameworks such as LHCb's Gaudi framework.