Processing Columnar Collider Data with GPU-Accelerated Kernels (1906.06242v2)

Abstract: At high energy physics experiments, processing billions of records of structured numerical data from collider events to a few statistical summaries is a common task. The data processing is typically more complex than standard query languages allow, such that custom numerical codes are used. At present, these codes mostly operate on individual event records and are parallelized in multi-step data reduction workflows using batch jobs across CPU farms. Based on a simplified top quark pair analysis with CMS Open Data, we demonstrate that it is possible to carry out significant parts of a collider analysis at a rate of around 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 of columns, and by expressing common analysis operations as kernels that can be used to process the event data in parallel. We find that only a small number of relatively simple functional kernels are needed for a generic HEP analysis. The approach based on columnar processing of data could speed up and simplify the cycle for delivering physics results at HEP experiments. We release the \texttt{hepaccelerate} prototype library as a demonstrator of such methods.