First Evaluation of the CPU, GPGPU and MIC Architectures for Real Time Particle Tracking based on Hough Transform at the LHC

Abstract

Recent innovations focused around {\em parallel} processing, either through systems containing multiple processors or processors containing multiple cores, hold great promise for enhancing the performance of the trigger at the LHC and extending its physics program. The flexibility of the CMS/ATLAS trigger system allows for easy integration of computational accelerators, such as NVIDIA's Tesla Graphics Processing Unit (GPU) or Intel's \xphi, in the High Level Trigger. These accelerators have the potential to provide faster or more energy efficient event selection, thus opening up possibilities for new complex triggers that were not previously feasible. At the same time, it is crucial to explore the performance limits achievable on the latest generation multicore CPUs with the use of the best software optimization methods. In this article, a new tracking algorithm based on the Hough transform will be evaluated for the first time on a multi-core Intel Xeon E5-2697v2 CPU, an NVIDIA Tesla K20c GPU, and an Intel \xphi\ 7120 coprocessor. Preliminary time performance will be presented.

13 pages, 4 figures, Accepted to JINST

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• Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs
• Massively Parallel Computing and the Search for Jets and Black Holes at the LHC
• Kalman-Filter-Based Particle Tracking on Parallel Architectures at Hadron Colliders
• Comparison of two hardware-based hit filtering methods for trackers in high-pileup environments
• Massively Parallel Computing at the Large Hadron Collider up to the HL-LHC
• Power-aware applications for scientific cluster and distributed computing
• Simulating collective neutrinos oscillations on the Intel Many Integrated Core (MIC) architecture