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v3.3.3-pre
Authors:
GeantV contributors list
Abstract:
SIMD acceleration can potentially boost by factors the application throughput. However, achieving efficient SIMD vectorization for scalar code with complex data flow and branching logic, goes way beyond breaking loop dependencies and relying on the compiler. Since the re-factoring effort scales with the number of lines of code, it is important to understand what kind of performance gains can be expected in such complex cases. The GeantV R&D has started a couple of years ago a top to bottom vectorization approach to particle transport simulation. Percolating multiple data to algorithms was mandatory since not all the components offer natural internal vectorization capability. Vectorizing low-level algorithms such as position/direction geometry classifiers or field propagators were certainly necessary, but not sufficient to achieve relevant SIMD gains. Overheads for maintaining the concurrent vector data flow and data copying had to be minimized. GeantV developed a framework to allow different categories of scalar and vectorized components to co-exist, dealing with data flow management and real-time heuristic optimizations. The paper will describe our approach on co-ordinating SIMD vectorization at framework level, making a detailed quantitative analysis of the SIMD gain versus overheads with a break-down by component in terms of geometry, physics and magnetic field propagation. The more general context of the GeantV work and goals for 2018 will also be presented.
Authors:
GeantV contributors list
Abstract:
The development of the GeantV Electromagnetic (EM) physics package has evolved following two necessary paths towards code modernization. A first phase required the revision of the main electromagnetic physics models and their implementation. The main objectives were to improve their accuracy, extend them to the new high-energy frontiers posed by the Future Circular Collider (FCC) programme and allow a better adaptation to a multi-particle flow. Most of the EM physics models in GeantV have been reviewed from theoretical perspective and rewritten with vector-friendly implementations, being now available in scalar mode in the alpha release. The second phase consists of an thorough investigation on the possibility to vectorise the most CPU-intensive physics code parts, such as final state sampling. We have shown the feasibility of implementing electromagnetic physics models that take advantage of SIMD/SIMT architectures, thus obtaining gains in performance. After this phase, the time has come for the GeantV project to take a step forward towards the final proof of concept. This takes shape through the testing of the full simulation chain (transport + physics + geometry) running in vectorised mode. In this paper we will present the first benchmark results obtained after vectorizing a set of electromagnetic physics models, starting from the photoelectric effect.
Type: Talk
Abstract:
Machine Learning techniques have been used in different applications by the HEP community: in this talk, we discuss the case of detector simulation. The amount of simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. We will describe an R&D activity, aimed at providing a configurable tool capable of training a neural network to reproduce the detector response and replace standard Monte Carlo simulation. This represents a generic approach in the sense that such a network could be designed and trained to simulate any kind of detector response. Eventually, it could be extended to replace the whole data processing chain in order to get, directly in one step, the final reconstructed quantities, in just a small fraction of time. We will present the first application of three-dimensional convolutional Generative Adversarial Networks to the simulation of high granularity electromagnetic calorimeters. We will describe detailed validation studies comparing our results to Geant4 Monte Carlo simulation, showing, in particular, the very good agreement we obtain for high level physics quantities (such as energy shower shapes) and detailed calorimeter response (single cell response). Finally we will show how this tool can easily be generalized to describe a larger class of calorimeters, opening the way to a generic machine learning based fast simulation approach.
Type: POSTER
Authors: VecGeom contributors
Abstract:
VecGeom is a multi-purpose geometry library targeting the optimisation of the 3D-solid's algorithms used extensively in particle transport and tracking applications. As a particular feature, the implementations of these algorithms are templated on the input data type and are explicitly vectorised using VecCore library in case of SIMD vector inputs. This provides additional performance for applications supporting a multi-particle flow, such as the GeantV prototype.
VecGeom became a fully-supported option in Geant4 since the release 10.2, replacing on demand the functionality provided by the native Geant4 solids. In this mode, VecGeom is called in scalar mode and SIMD benefits can be achieved by internal vectorization of the geometry algorithms allowing it. This approach has proven to bring very large benefits for the tessellated solids represented in terms of triangular facets.
To expose more vectorization in the scalar mode we have extended the approach used for the triangular tessellations to other multi-faceted shapes, such as the extruded polygon, the polyhedra and different trapezoids. The paper will present the strategy used to vectorise the different processing phases for tessellated solids, the performance improvements compared to the previous scalar implementations for other solids using this approach, and how this is reflected in Geant4 simulations using VecGeom as geometry engine.
Abstract
One of the key factors for the successful development of a physics Monte-Carlo is the ability to properly organize regression testing and validation. Geant4, a world-standard toolkit for HEP detector simulation, is one such example that requires thorough validation. The CERN/SFT group, which contributes to the development, testing, deployment and support of the toolkit, is also responsible for the monthly validation of the development releases of Geant4, based on a set of community-developed tests.
In this talk we present the web application "geant-val" developed specifically to analyse and visualise results whilst comparing them between different Geant4 releases. The application is written using the Node.js and Angular frameworks, and uses PostgreSQL for storing test results. The output is visualised using ROOT and JSROOT. In addition to a pure visual comparison, we perform different statistical tests (chi-square, Kolmogorov-Smirnov etc) on the client side using HTML5 Web Workers.
The "geant-val" application has been demonstrated to be a robust tool for the regression testing and validation of the Geant4 toolkit. The generic design is such that it can be applied to compare any histograms no matter from which Monte-Carlo code/generator the data were produced.