Rapid event extraction and tensorial event adaption.
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| Title: | Rapid event extraction and tensorial event adaption. |
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| Authors: | Roiser, Stefan1 (AUTHOR) stefan.roiser@cern.ch, Schöfbeck, Robert2 (AUTHOR) robert.schoefbeck@oeaw.ac.at, Wettersten, Zenny1,2 (AUTHOR) zenny.wettersten@cern.ch |
| Source: | European Physical Journal C -- Particles & Fields. Dec2025, Vol. 85 Issue 12, p1-31. 31p. |
| Subjects: | Event processing (Computer science), Parallel processing, Scattering (Physics) |
| Abstract: | We present Rex and teaRex, C++17 libraries for efficient management of parton-level hard scattering event information and completely generic reweighting of such events, respectively. Rex is primarily an interfacing and I/O library for Les Houches Event format files and provides an internal event format designed with data parallelism in mind, and teaRex extends this format to provide full parton-level reweighting functionality with minimal code needing to be written by the end user. These libraries serve as the foundation for the MadtRex reweighting module for MadGraph5_aMC@NLO, extending the functionality of the CUDACPP plugin to allow for data-parallel model-generic leading order parameter reweighting with on-CPU hardware acceleration and GPU offloading, speeding up reweighting by more than two orders of magnitude compared to MadGraph5_aMC@NLO running on the exact same hardware while providing trivial scalability to larger and distributed systems. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | We present Rex and teaRex, C++17 libraries for efficient management of parton-level hard scattering event information and completely generic reweighting of such events, respectively. Rex is primarily an interfacing and I/O library for Les Houches Event format files and provides an internal event format designed with data parallelism in mind, and teaRex extends this format to provide full parton-level reweighting functionality with minimal code needing to be written by the end user. These libraries serve as the foundation for the MadtRex reweighting module for MadGraph5_aMC@NLO, extending the functionality of the CUDACPP plugin to allow for data-parallel model-generic leading order parameter reweighting with on-CPU hardware acceleration and GPU offloading, speeding up reweighting by more than two orders of magnitude compared to MadGraph5_aMC@NLO running on the exact same hardware while providing trivial scalability to larger and distributed systems. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 14346044 |
| DOI: | 10.1140/epjc/s10052-025-15108-7 |
