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The upgraded CERN LHCb detector, due to start data taking in 2021, will haveto reconstruct 4 TB/s of raw detector data in real time using commodityprocessors. This is one of the biggest real-time data processing challenges inany scientific domain. We present an intrinsically parallel reconstructionalgorithm for the vertex detector of the LHCb experiment designed to optimallyexploit multi-core general purpose architectures. We compare it to previousstate-of-the-art scalar pattern recognition algorithms and show significantlyfaster processing and in some cases increased physics performance over allcurrent alternatives. We evaluate the algorithm on two high-end architecturesfrom two different vendors and discuss in detail the impact of different SIMDInstruction Set Architecture extensions on the performance.

A fast and efficient SIMD track reconstruction algorithm for the LHCb Upgrade 1 VELO-PIX detector