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.
Performance is a critical issue in a production system accommodating hundreds of analysis users. Compared to a local session, distributed analysis is exposed to services and network latencies, remote ...data access and heterogeneous computing infrastructure, creating a more complex performance and efficiency optimization matrix. During the last 2 years, ALICE analysis shifted from a fast development phase to the more mature and stable code. At the same time, the frameworks and tools for deployment, monitoring and management of large productions have evolved considerably too. The ALICE Grid production system is currently used by a fair share of organized and individual user analysis, consuming up to 30% or the available resources and ranging from fully I/O-bound analysis code to CPU intensive correlations or resonances studies. While the intrinsic analysis performance is unlikely to improve by a large factor during the LHC long shutdown (LS1), the overall efficiency of the system has still to be improved by an important factor to satisfy the analysis needs. We have instrumented all analysis jobs with "sensors" collecting comprehensive monitoring information on the job running conditions and performance in order to identify bottlenecks in the data processing flow. This data are collected by the MonALISa-based ALICE Grid monitoring system and are used to steer and improve the job submission and management policy, to identify operational problems in real time and to perform automatic corrective actions. In parallel with an upgrade of our production system we are aiming for low level improvements related to data format, data management and merging of results to allow for a better performing ALICE analysis.
We have developed an interface within the ALICE analysis framework that allows transparent usage of the experiment's distributed resources. This analysis plug-in makes it possible to configure ...back-end specific parameters from a single interface and to run with no change the same custom user analysis in many computing environments, from local workstations to PROOF clusters or GRID resources. The tool is used now extensively in the ALICE collaboration for both end-user analysis and large scale productions.
Open access is one of the important leverages for long-term data preservation for a HEP experiment. To guarantee the usability of data analysis tools beyond the experiment lifetime it is crucial that ...third party users from the scientific community have access to the data and associated software. The ALICE Collaboration has developed a layer of lightweight components built on top of virtualization technology to hide the complexity and details of the experiment-specific software. Users can perform basic analysis tasks within CernVM, a lightweight generic virtual machine, paired with an ALICE specific contextualization. Once the virtual machine is launched, a graphical user interface is automatically started without any additional configuration. This interface allows downloading the base ALICE analysis software and running a set of ALICE analysis modules. Currently the available tools include fully documented tutorials for ALICE analysis, such as the measurement of strange particle production or the nuclear modification factor in Pb-Pb collisions. The interface can be easily extended to include an arbitrary number of additional analysis modules. We present the current status of the tools used by ALICE through the CERN open access portal, and the plans for future extensions of this system.
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.
The recent emergence of hardware architectures characterized by many-core or accelerated processors has opened new opportunities for concurrent programming models taking advantage of both SIMD and ...SIMT architectures. GeantV, a next generation detector simulation, has been designed to exploit both the vector capability of mainstream CPUs and multi-threading capabilities of coprocessors including NVidia GPUs and Intel Xeon Phi. The characteristics of these architectures are very different in terms of the vectorization depth and type of parallelization needed to achieve optimal performance. In this paper we describe implementation of electromagnetic physics models developed for parallel computing architectures as a part of the GeantV project. Results of preliminary performance evaluation and physics validation are presented as well.