  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.

Distributed, Parallel, and Cluster Computing