hepaccelerate: Fast Analysis of Columnar Collider Data

Abstract

At HEP experiments, processing terabytes of structured numerical event data to a few statistical summaries is a common task. This step involves selecting events and objects within the event, reconstructing high-level variables, evaluating multivariate classifiers with up to hundreds of variations and creating thousands of low-dimensional histograms. Currently, this is done using multi-step workflows and batch jobs. Based on the CMS search for H(μμ), we demonstrate that it is possible to carry out significant parts of a real collider analysis at a rate of up to a million events per second on a single multicore server with optional GPU acceleration. This is achieved by representing HEP event data as memory-mappable sparse arrays, and by expressing common analysis operations as kernels that can be parallelized across the data using multithreading. We find that only a small number of relatively simple kernels are needed to implement significant parts of this Higgs analysis. Therefore, analysis of real collider datasets of billions events could be done within minutes to a few hours using simple multithreaded codes, reducing the need for managing distributed workflows in the exploratory phase. This approach could speed up the cycle for delivering physics results at HEP experiments. We release the hepaccelerate prototype library as a demonstrator of such accelerated computational kernels. We look forward to discussion, further development and use of efficient and easy-to-use software for terabyte-scale high-level data analysis in the physical sciences.

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