2022/07/18 by Eich, Niclas, Erdmann, Martin, Fackeldey, Peter +3
#Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.2207.08598
The development of an LHC physics analysis involves numerous investigations that require the repeated processing of terabytes of data. Thus, a rapid completion of each of these analysis cycles is central to mastering the science project. We present a solution to efficiently handle and accelerate physics analyses on small-size institute clusters. Our solution is based on three key concepts: Vectorized processing of collision events, the "MapReduce" paradigm for scaling out on computing clusters, and efficiently utilized SSD caching to reduce latencies in IO operations. Using simulations from a Higgs pair production physics analysis as an example, we achieve an improvement factor of 6.3 in runtime after one cycle and even an overall speedup of a factor of 14.9 after 10 cycles.