Increasing Parallelism in the ROOT I/O Subsystem

TL;DR

This paper discusses enhancements to the ROOT I/O subsystem to improve parallel data processing, enabling better scalability and performance in high energy physics workflows on various hardware architectures.

Contribution

It introduces specific improvements to ROOT I/O that enhance multithreaded scalability and performance in data reading and writing operations.

Findings

Significant performance gains in multithreaded ROOT I/O operations.

Effective parallelism in (de)compression, (de)serialization, and storage access.

Demonstrated scalability on traditional and highly parallel architectures.

Abstract

When processing large amounts of data, the rate at which reading and writing can take place is a critical factor. High energy physics data processing relying on ROOT is no exception. The recent parallelisation of LHC experiments' software frameworks and the analysis of the ever increasing amount of collision data collected by experiments further emphasized this issue underlying the need of increasing the implicit parallelism expressed within the ROOT I/O. In this contribution we highlight the improvements of the ROOT I/O subsystem which targeted a satisfactory scaling behaviour in a multithreaded context. The effect of parallelism on the individual steps which are chained by ROOT to read and write data, namely (de)compression, (de)serialisation, access to storage backend, are discussed. Performance measurements are discussed through real life examples coming from CMS production workflows on traditional server platforms and highly parallel architectures such as Intel Xeon Phi.