Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUs

Valassi, A.; Roiser, S.; Mattelaer, Olivier; Hageboeck, Stephan

doi:10.1051/epjconf/202125103045

Cited by 14 publications

(13 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Working Group has already identified three GPUcapable HEP applications and created prototype HEP-Workloads container images for each application: (i) a simulation of particle trajectories in the LHC by the CERN Accelerator Group [32]; (ii) a physics event generator application [33]; and (iii) the CMS HLT online reconstruction [34]. Currently, these applications are not part of any WLCG production activity and, for this reason, were not included in HEPscore .…”

Section: Discussionmentioning

confidence: 99%

HEPiX Benchmarking Solution for WLCG Computing Resources

Giordano

Alef

Atzori

et al. 2021

Comput Softw Big Sci

Self Cite

View full text Add to dashboard Cite

The HEPiX Benchmarking Working Group has developed a framework to benchmark the performance of a computational server using the software applications of the High Energy Physics (HEP) community. This framework consists of two main components, named HEP-Workloads and HEPscore. HEP-Workloads is a collection of standalone production applications provided by a number of HEP experiments. HEPscore is designed to run HEP-Workloads and provide an overall measurement that is representative of the computing power of a system. HEPscore is able to measure the performance of systems with different processor architectures and accelerators. The framework is completed by the HEP Benchmark Suite that simplifies the process of executing HEPscore and other benchmarks such as HEP-SPEC06, SPEC CPU 2017, and DB12. This paper describes the motivation, the design choices, and the results achieved by the HEPiX Benchmarking Working group. A perspective on future plans is also presented.

show abstract

Section: Discussionmentioning

confidence: 99%

HEPiX Benchmarking Solution for WLCG Computing Resources

Giordano

Alef

Atzori

et al. 2021

Comput Softw Big Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…Each group takes a different approach in handling the conversion from CPU codes to GPU codes. These approaches are the ones developed in BlockGen [24], MadFlow [25], and MadGraph-GPU [26].…”

Section: Gpu Matrix Element Generatorsmentioning

confidence: 99%

Generators and the (Accelerated) Future

Isaacson

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

With the High Luminosity LHC coming online in the near future, event generators will need to provide very large event samples to match the experimental precision. Currently, the estimated cost to generate these events exceeds the computing budget of the LHC experiments. To address these issues, the computing efficiency of event generators need to be improved. Many different approaches are being taken to achieve this goal. I will cover the ongoing work on implementing event generators on the GPUs, machine learning the matrix element, machine learning the phase space, and minimizing the number of negative weight events.

show abstract

“…One promising development is the ongoing work [876] on reengineering the matrix element calculation in MADGRAPH5 aMC@NLO for GPUs and vector CPUs. With respect to a single threaded non-vectorized CPU version on a modern Intel CPU, in double precision a throughput increase of a factor ∼4 was obtained on a modern Intel CPU through SIMD vectorization compared to a single-threaded non-vectorized implementation for C++ double precision calculations.…”

Section: Event Weights For Systematic Uncertaintiesmentioning

confidence: 99%