1,180 research outputs found
A Portable Parton-Level Event Generator for the High-Luminosity LHC
Parton-level event generators are one of the most computationally demanding
parts of the simulation chain for the Large Hadron Collider. The rapid
deployment of computing hardware different from the traditional CPU+RAM model
in data centers around the world mandates a change in event generator design.
These changes are required in order to provide economically and ecologically
sustainable simulations for the high-luminosity era of the LHC. We present the
first complete leading-order parton-level event generation framework capable of
utilizing most modern hardware. Furthermore, we discuss its performance in the
standard candle processes of vector boson and top-quark pair production with up
to five additional jets.Comment: Submission to SciPost, 23 pages, 7 figures, 2 table
Dynamic Glyphs: Appropriating Causality Perception in Multivariate Visual Analysis
We investigate how to co-opt the perception of causality to aid the analysis of multivariate data. We propose Dynamic Glyphs (DyGs), an animated extension to traditional glyphs. DyGs encode data relations through seemingly physical interactions between glyph parts. We hypothesize that this representation gives rise to impressions of causality, enabling observers to reason intuitively about complex, multivariate dynamics. In a crowdsourced experiment, participants' accuracy with DyGs exceeded or was comparable to non-animated alternatives. Moreover, participants showed a propensity to infer higher-dimensional relations with DyGs. Our findings suggest that visual causality can be an effective 'channel' for communicating complex data relations that are otherwise difficult to think about. We discuss the implications and highlight future research opportunities
Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations
Visualization tools facilitate exploratory data analysis, but fall short at supporting hypothesis-based reasoning. We conducted an exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data. We report on how participants leveraged these unique affordances for visual analysis. We found that a majority of participants articulated meaningful models and predictions, utilizing them as entry points to sensemaking. We contribute an abstract typology representing the types of models participants held and externalized as data expectations. Our findings suggest ways for rearchitecting visual analytics tools to better support hypothesis- and model-based reasoning, in addition to their traditional role in exploratory analysis. We discuss the design implications and reflect on the potential benefits and challenges involved.National Science Foundation award #175561
Towards Concept-Driven Visual Analytics
Visualizations of data provide a proven method for analysts to explore and make data-driven discoveries. However, current visualization tools provide only limited support for hypothesis-driven analyses, and often lack capabilities that would allow users to visually test the fit of their conceptual models against the data. This imbalance could bias users to overly rely on exploratory visual analysis as the principal mode of inquiry, which can be detrimental to discovery. To address this gap, we propose a new paradigm for 'concept-driven' visual analysis. In this style of analysis, analysts share their conceptual models and hypotheses with the system. The system then uses those inputs to drive the generation of visualizations, while providing plots and interactions to explore places where models and data disagree. We discuss key characteristics and design considerations for concept-driven visualizations, and report preliminary findings from a formative study.National Science Foundation award #175561
Efficient phase-space generation for hadron collider event simulation
We present a simple yet efficient algorithm for phase-space integration at
hadron colliders. Individual mappings consist of a single t-channel combined
with any number of s-channel decays, and are constructed using diagrammatic
information. The factorial growth in the number of channels is tamed by
providing an option to limit the number of s-channel topologies. We provide a
publicly available, parallelized code in C++ and test its performance in
typical LHC scenarios.Comment: 11 pages, 3 figure
Efficient precision simulation of processes with many-jet final states at the LHC
We present a scalable technique for the simulation of collider events with
multi-jet final states, based on an improved parton-level event file format.
The method is implemented for both leading- and next-to-leading order QCD
calculations. We perform a comprehensive analysis of the I/O performance and
validate our new framework using Higgs-boson plus multi-jet production with up
to seven jets. We make the resulting code base available for public use.Comment: 14 pages, 7 figures, 2 table
Speeding up Madgraph5 aMC@NLO through CPU vectorization and GPU offloading: towards a first alpha release
The matrix element (ME) calculation in any Monte Carlo physics event
generator is an ideal fit for implementing data parallelism with lockstep
processing on GPUs and vector CPUs. For complex physics processes where the ME
calculation is the computational bottleneck of event generation workflows, this
can lead to large overall speedups by efficiently exploiting these hardware
architectures, which are now largely underutilized in HEP. In this paper, we
present the status of our work on the reengineering of the Madgraph5_aMC@NLO
event generator at the time of the ACAT2022 conference. The progress achieved
since our previous publication in the ICHEP2022 proceedings is discussed, for
our implementations of the ME calculations in vectorized C++, in CUDA and in
the SYCL framework, as well as in their integration into the existing MadEvent
framework. The outlook towards a first alpha release of the software supporting
QCD LO processes usable by the LHC experiments is also discussed.Comment: 7 pages, 4 figures, 4 tables; submitted to ACAT 2022 proceedings in
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Evaluating Portable Parallelization Strategies for Heterogeneous Architectures in High Energy Physics
High-energy physics (HEP) experiments have developed millions of lines of
code over decades that are optimized to run on traditional x86 CPU systems.
However, we are seeing a rapidly increasing fraction of floating point
computing power in leadership-class computing facilities and traditional data
centers coming from new accelerator architectures, such as GPUs. HEP
experiments are now faced with the untenable prospect of rewriting millions of
lines of x86 CPU code, for the increasingly dominant architectures found in
these computational accelerators. This task is made more challenging by the
architecture-specific languages and APIs promoted by manufacturers such as
NVIDIA, Intel and AMD. Producing multiple, architecture-specific
implementations is not a viable scenario, given the available person power and
code maintenance issues.
The Portable Parallelization Strategies team of the HEP Center for
Computational Excellence is investigating the use of Kokkos, SYCL, OpenMP,
std::execution::parallel and alpaka as potential portability solutions that
promise to execute on multiple architectures from the same source code, using
representative use cases from major HEP experiments, including the DUNE
experiment of the Long Baseline Neutrino Facility, and the ATLAS and CMS
experiments of the Large Hadron Collider. This cross-cutting evaluation of
portability solutions using real applications will help inform and guide the
HEP community when choosing their software and hardware suites for the next
generation of experimental frameworks. We present the outcomes of our studies,
including performance metrics, porting challenges, API evaluations, and build
system integration.Comment: 18 pages, 9 Figures, 2 Table
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