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The Geant4 simulation toolkit has reached maturity in the middle of the previous decade, providing a wide variety of established features coherently aggregated in a software product, which has become the standard for detector simulation in HEP and is used in a variety of other application domains. We review the most recent capabilities introduced in the kernel, highlighting those, which are being prepared for the next major release (version 10.0) that is scheduled for the end of 2013. A significant new feature contained in this release will be the integration of multi-threading processing, aiming at targeting efficient use of modern many-cores system architectures and minimization of the memory footprint for exploiting event-level parallelism. We discuss its design features and impact on the existing API and user-interface of Geant4. Revisions are made to balance the need for preserving backwards compatibility and to consolidate and improve the interfaces; taking into account requirements from the multithreaded extensions and from the evolution of the data processing models of the LHC experiments.

Making general particle transport simulation for high-energy physics (HEP) single-instruction-multiple-thread (SIMT) friendly, to take advantage of accelerator hardware, is an important alternative for boosting the throughput of simulation applications. To date, this challenge is not yet resolved, due to difficulties in mapping the complexity of Geant4 components and workflow to the massive parallelism features exposed by graphics processing units (GPU). The AdePT project is one of the R&D initiatives tackling this limitation and exploring GPUs as potential accelerators for offloading some part of the CPU simulation workload. Our main target is to implement a complete electromagnetic shower demonstrator working on the GPU. The project is the first to create a full prototype of a realistic electron, positron, and gamma electromagnetic shower simulation on GPU, implemented as either a standalone application or as an extension of the standard Geant4 CPU workflow. Our prototype currently provides a platform to explore many optimisations and different approaches. We present the most recent results and initial conclusions of our work, using both a standalone GPU performance analysis and a first implementation of a hybrid workflow based on Geant4 on the CPU and AdePT on the GPU.

Over the past few years, our knowledge of how ecological interactions shape the structure and dynamics of natural communities has rapidly advanced. Plant chemical traits play key roles in these processes because they mediate a diverse range of direct and indirect interactions in a community-wide context. Many chemically mediated interactions have been extensively studied in industrial cropping systems, and thus have focused on simplified, pairwise and linear interactions that rarely incorporate a community perspective. A contrasting approach considers the agroecosystem as a functioning whole, in which food production occurs. It offers an opportunity to better understand how plant chemical traits mediate complex interactions which can enhance or hinder ecosystem functions. In this paper, we argue that studying chemically mediated interactions in agroecosystems is essential to comprehend how agroecosystem services emerge and how they can be guaranteed through ecosystem management. First, we discuss how plant chemical traits affect and are affected by ecological interactions. We then explore research questions and future directions on how studying chemical mediation in complex agroecosystems can help us understand the emergence and management of ecosystem services, specifically biological control and pollination.

Ecological interactions are one of the main forces that sustain Earth’s biodiversity. A major challenge for studies of ecology and evolution is to determine how these interactions affect the fitness of species when we expand from studying isolated, pairwise interactions to include networks of interacting species 1 – 4 . In networks, chains of effects caused by a range of species have an indirect effect on other species they do not interact with directly, potentially affecting the fitness outcomes of a variety of ecological interactions (such as mutualism) 5 – 7 . Here we apply analytical techniques and numerical simulations to 186 empirical mutualistic networks and show how both direct and indirect effects alter the fitness of species coevolving in these networks. Although the fitness of species usually increased with the number of mutualistic partners, most of the fitness variation across species was driven by indirect effects. We found that these indirect effects prevent coevolving species from adapting to their mutualistic partners and to other sources of selection pressure in the environment, thereby decreasing their fitness. Such decreases are distributed in a predictable way within networks: peripheral species receive more indirect effects and experience higher reductions in fitness than central species. This topological effect was also evident when we analysed an empirical study of an invasion of pollination networks by honeybees. As honeybees became integrated as a central species within networks, they increased the contribution of indirect effects on several other species, reducing their fitness. Our study shows how and why indirect effects can govern the adaptive landscape of species-rich mutualistic assemblages. A numerical analysis of mutualistic interactions between species shows that indirect effects from species they do not interact with directly are the biggest source of variation and cause the largest decreases to species fitness.

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.

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.

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.

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.

Insect seasonality is a known pattern that has intrigued ecologists for over 30 years. However, despite being well understood in general, for several taxa such as Lepidopteran caterpillars its underlying causes and mechanisms are still not fully understood. This is especially critical for Brazilian tropical forests where caterpillars have previously been shown to have a puzzling pattern of peaking in abundance only in the first months of the dry season; however, this pattern still lacks an explanation. Here, to advance our understanding of the factors underlying seasonal changes in caterpillar abundance in tropical forests, we addressed how the lepidopteran caterpillar community that feeds on Piper amalago L. plants, their host plants leaf numbers, the herbivory levels, and the parasitoid pressure all change throughout the dry and wet seasons in a Brazilian tropical semideciduous forest. We found that immature abundance and herbivory peak in the first 2 months of the dry season and then rapidly decrease and remain low throughout the remaining of the dry season and the entire wet season at the study site. However, although the proportion of parasitized immatures increased alongside caterpillar abundance, it peaked in the month that followed a drastic decrease in caterpillar abundance. These results suggest that parasitoids play a major role in the observed caterpillar abundance pattern and thus, we propose the hypothesis that high parasitoid predation pressure causes early eclosion and emergence of caterpillars and primarily drives caterpillar abundance seasonality in Brazilian tropical forests.