Evolution of Data Structures for Heterogeneous Reconstruction in CMSSW

The Next Generation Trigger project aims to improve the computational efficiency of the CMS reconstruction software (CMSSW) to increase the data processing throughput at the High-Luminosity Large Hadron Collider. As part of this project, this work focuses on improving the common Structure of Arrays (SoA) used in CMSSW for running both on CPUs and GPUs. We introduce a new SoA feature that allows users to selectively prune and combine columns across one or more existing SoAs into a new view, while preserving a user-friendly interface. It is also possible to consolidate these columns into a new SoA object, performing heterogeneous memory copies as needed. This process uses the Alpaka library for optimizing data transfer across different computing architectures, reducing overhead, and improving efficiency. Another new feature introduces the possibility of generating custom methods for SoA elements, enhancing flexibility and expressiveness in data manipulation. The design prioritizes ease of use, allowing users to interact with the data intuitively while benefiting from an efficient underlying implementation. The impact of these optimizations, along with performance measurements, will be presented.

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Publication Details

Published
2025-09-08
DOI
https://doi.org/10.17181/kd13h-42e08
Primary Topic
Particle Detector Development and Performance
Type
article
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article

Evolution of Data Structures for Heterogeneous Reconstruction in CMSSW

Particle Detector Development and Performance
article

Evolution of Data Structures for Heterogeneous Reconstruction in CMSSW

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Abstract

The Next Generation Trigger project aims to improve the computational efficiency of the CMS reconstruction software (CMSSW) to increase the data processing throughput at the High-Luminosity Large Hadron Collider. As part of this project, this work focuses on improving the common Structure of Arrays (SoA) used in CMSSW for running both on CPUs and GPUs. We introduce a new SoA feature that allows users to selectively prune and combine columns across one or more existing SoAs into a new view, while preserving a user-friendly interface. It is also possible to consolidate these columns into a new SoA object, performing heterogeneous memory copies as needed. This process uses the Alpaka library for optimizing data transfer across different computing architectures, reducing overhead, and improving efficiency. Another new feature introduces the possibility of generating custom methods for SoA elements, enhancing flexibility and expressiveness in data manipulation. The design prioritizes ease of use, allowing users to interact with the data intuitively while benefiting from an efficient underlying implementation. The impact of these optimizations, along with performance measurements, will be presented.

European Organization for Nuclear Research (CH), Politecnico di Milano (IT)
Openalex Percentile: Top 26%
Particle Detector Development and Performance
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