Abstract
In the coming years, HEP data processing will need to exploit parallelism on present and future hardware resources to sustain the bandwidth requirements. As one of the cornerstones of the HEP software ecosystem, ROOT embraced an ambitious parallelisation plan which delivered compelling results. In this contribution the strategy is characterised as well as its evolution in the medium term. The units of the ROOT framework are discussed where task and data parallelism have been introduced, with runtime and scaling measurements. We will give an overview of concurrent operations in ROOT, for instance in the areas of I/O (reading and writing of data), fitting / minimization, and data analysis. This paper introduces the programming model and use cases for explicit and implicit parallelism, where the former is explicit in user code and the latter is implicitly managed by ROOT internally.
Highlights
In the coming years, High Energy Physics (HEP) data processing will need to exploit parallelism on present and future hardware resources to sustain the bandwidth requirements
The processing of the data in the High Luminosity LHC [1] era poses unprecedented challenges to computation in High Energy Physics (HEP)
Given present and realistically foreseeable computer hardware, what is certain is that the exploitation of parallelism in HEP code, from simulation to end user analysis is a prerequisite for our success
Summary
The processing of the data in the High Luminosity LHC [1] era poses unprecedented challenges to computation in High Energy Physics (HEP). The data produced by LHC [2] experiments will increase by one order of magnitude. The dataset will be significantly more complex than the one of today due to the dramatic rise of number of simultaneous collisions per bunch crossing. Several strategies to fit the expected budget envelope while honouring the foreseen ambitious Physics programme are being discussed. Given present and realistically foreseeable computer hardware, what is certain is that the exploitation of parallelism in HEP code, from simulation to end user analysis is a prerequisite for our success
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