In the fall 2016, GeantV went through a thorough community evaluation of the project status and of its strategy for sharing the R&D results with the LHC experiments and with the HEP simulation ...community in general. Following this discussion, GeantV has engaged onto an ambitious 2-year road-path aiming to deliver a beta version that has most of the final design and several performance features of the final product, partially integrated with some of the experiment's frameworks. The initial GeantV prototype has been updated to a vector-aware concurrent framework, which is able to deliver high-density floating-point computations for most of the performance-critical components such as propagation in field and physics models. Electromagnetic physics models were adapted for the specific GeantV requirements, aiming for the full demonstration of shower physics performance in the alpha release at the end of 2017. We have revisited and formalized GeantV user interfaces and helper protocols, allowing to: connect to user code, provide recipes to access efficiently MC truth and generate user data in a concurrent environment.
Thread-parallelisation and single-instruction multiple data (SIMD) "vectorisation" of software components in HEP computing has become a necessity to fully benefit from current and future computing ...hardware. In this context, the Geant-Vector GPU simulation project aims to re-engineer current software for the simulation of the passage of particles through detectors in order to increase the overall event throughput. As one of the core modules in this area, the geometry library plays a central role and vectorising its algorithms will be one of the cornerstones towards achieving good CPU performance. Here, we report on the progress made in vectorising the shape primitives, as well as in applying new C++ template based optimisations of existing code available in the Geant4, ROOT or USolids geometry libraries. We will focus on a presentation of our software development approach that aims to provide optimised code for all use cases of the library (e.g., single particle and many-particle APIs) and to support different architectures (CPU and GPU) while keeping the code base small, manageable and maintainable. We report on a generic and templated C++ geometry library as a continuation of the AIDA USolids project. The experience gained with these developments will be beneficial to other parts of the simulation software, such as for the optimisation of the physics library, and possibly to other parts of the experiment software stack, such as reconstruction and analysis.
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.
The GeantV project is focused on the R&D of new particle transport techniques to maximize parallelism on multiple levels, profiting from the use of both SIMD instructions and co-processors for the ...CPU-intensive calculations specific to this type of applications. In our approach, vectors of tracks belonging to multiple events and matching different locality criteria must be gathered and dispatched to algorithms having vector signatures. While the transport propagates tracks and changes their individual states, data locality becomes harder to maintain. The scheduling policy has to be changed to maintain efficient vectors while keeping an optimal level of concurrency. The model has complex dynamics requiring tuning the thresholds to switch between the normal regime and special modes, i.e. prioritizing events to allow flushing memory, adding new events in the transport pipeline to boost locality, dynamically adjusting the particle vector size or switching between vector to single track mode when vectorization causes only overhead. This work requires a comprehensive study for optimizing these parameters to make the behaviour of the scheduler self-adapting, presenting here its initial results.
An intensive R&D and programming effort is required to accomplish new challenges posed by future experimental high-energy particle physics (HEP) programs. The GeantV project aims to narrow the gap ...between the performance of the existing HEP detector simulation software and the ideal performance achievable, exploiting latest advances in computing technology. The project has developed a particle detector simulation prototype capable of transporting in parallel particles in complex geometries exploiting instruction level microparallelism (SIMD and SIMT), task-level parallelism (multithreading) and high-level parallelism (MPI), leveraging both the multi-core and the many-core opportunities. We present preliminary verification results concerning the electromagnetic (EM) physics models developed for parallel computing architectures within the GeantV project. In order to exploit the potential of vectorization and accelerators and to make the physics model effectively parallelizable, advanced sampling techniques have been implemented and tested. In this paper we introduce a set of automated statistical tests in order to verify the vectorized models by checking their consistency with the corresponding Geant4 models and to validate them against experimental data.
GeantV Amadio, G.; Ananya, A.; Apostolakis, J. ...
Computing and software for big science,
12/2021, Letnik:
5, Številka:
1
Journal Article
Odprti dostop
Full detector simulation was among the largest CPU consumers in all CERN experiment software stacks for the first two runs of the Large Hadron Collider. In the early 2010s, it was projected that ...simulation demands would scale linearly with increasing luminosity, with only partial compensation from increasing computing resources. The extension of fast simulation approaches to cover more use cases that represent a larger fraction of the simulation budget is only part of the solution, because of intrinsic precision limitations. The remainder corresponds to speeding up the simulation software by several factors, which is not achievable by just applying simple optimizations to the current code base. In this context, the GeantV R&D project was launched, aiming to redesign the legacy particle transport code in order to benefit from features of fine-grained parallelism, including vectorization and increased locality of both instruction and data. This paper provides an extensive presentation of the results and achievements of this R&D project, as well as the conclusions and lessons learned from the beta version prototype.
GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions ...with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) and handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. The goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.
Performance of GeantV EM Physics Models Amadio, G; Ananya, A; Apostolakis, J ...
Journal of physics. Conference series,
10/2017, Letnik:
898, Številka:
7
Journal Article
Recenzirano
Odprti dostop
The recent progress in parallel hardware architectures with deeper vector pipelines or many-cores technologies brings opportunities for HEP experiments to take advantage of SIMD and SIMT computing ...models. Launched in 2013, the GeantV project studies performance gains in propagating multiple particles in parallel, improving instruction throughput and data locality in HEP event simulation on modern parallel hardware architecture. Due to the complexity of geometry description and physics algorithms of a typical HEP application, performance analysis is indispensable in identifying factors limiting parallel execution. In this report, we will present design considerations and preliminary computing performance of GeantV physics models on coprocessors (Intel Xeon Phi and NVidia GPUs) as well as on mainstream CPUs.
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