Vectorising the detector geometry to optimize particle transport

TL;DR

This paper explores how vector instruction set extensions can optimize particle transport geometry navigation, moving beyond scalar methods to improve performance in complex detector simulations.

Contribution

It investigates vectorizing critical geometry algorithms in ROOT, demonstrating initial benchmarks and proposing a vector-based navigation approach for particle transport.

Findings

Vectorization improves geometry algorithm performance.

Initial benchmarks show promising speedups.

A prototype vector navigator is developed and tested.

Abstract

Among the components contributing to particle transport, geometry navigation is an important consumer of CPU cycles. The tasks performed to get answers to "basic" queries such as locating a point within a geometry hierarchy or computing accurately the distance to the next boundary can become very computing intensive for complex detector setups. So far, the existing geometry algorithms employ mainly scalar optimisation strategies (voxelization, caching) to reduce their CPU consumption. In this paper, we would like to take a different approach and investigate how geometry navigation can benefit from the vector instruction set extensions that are one of the primary source of performance enhancements on current and future hardware. While on paper, this form of microparallelism promises increasing performance opportunities, applying this technology to the highly hierarchical and multiply branched geometry code is a difficult challenge. We refer to the current work done to vectorise an important part of the critical navigation algorithms in the ROOT geometry library. Starting from a short critical discussion about the programming model, we present the current status and first benchmark results of the vectorisation of some elementary geometry shape algorithms. On the path towards a full vector-based geometry navigator, we also investigate the performance benefits in connecting these elementary functions together to develop algorithms which are entirely based on the flow of vector-data. To this end, we discuss core components of a simple vector navigator that is tested and evaluated on a toy detector setup.