Abstract
Detector simulation is consuming at least half of the HEP computing cycles, and even so, experiments have to take hard decisions on what to simulate, as their needs greatly surpass the availability of computing resources. New experiments still in the design phase such as FCC, CLIC and ILC as well as upgraded versions of the existing LHC detectors will push further the simulation requirements. Since the increase in computing resources is not likely to keep pace with our needs, it is therefore necessary to explore innovative ways of speeding up simulation in order to sustain the progress of High Energy Physics. The GeantV project aims at developing a high performance detector simulation system integrating fast and full simulation that can be ported on different computing architectures, including CPU accelerators. After more than two years of R&D the project has produced a prototype capable of transporting particles in complex geometries exploiting micro-parallelism, SIMD and multithreading. Portability is obtained via C++ template techniques that allow the development of machine- independent computational kernels. A set of tables derived from Geant4 for cross sections and final states provides a realistic shower development and, having been ported into a Geant4 physics list, can be used as a basis for a direct performance comparison.
Highlights
High Energy Physics needs ever-increasing computing power
Starting from the USolids library [7], which was developed in the framework of the AIDA project, we have developed a high performance library (VecGeom) of geometrical routines with vector signature [8, 9]
To verify whether the basket management does not offset the possible SIMD gains in physics and geometry, we have implemented a single threaded version of our basket scheduler and we have exercised it on the full CMS geometry with TGeo, since the VecGeom navigation is not yet fully validated on the complete CMS detector geometry
Summary
- GeantV: from CPU to accelerators G Amadio, A Ananya, J Apostolakis et al. - Adaptive track scheduling to optimize concurrency and vectorization in GeantV J Apostolakis, M Bandieramonte, G Bitzes et al. - GeantV: from CPU to accelerators G Amadio, A Ananya, J Apostolakis et al. - Adaptive track scheduling to optimize concurrency and vectorization in GeantV J Apostolakis, M Bandieramonte, G Bitzes et al. - Verification of Electromagnetic Physics Models for Parallel Computing Architectures in the GeantV Project G Amadio, J Apostolakis, M Bandieramonte et al
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