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Portable and efficient vectorization is a significant challenge in large software projects such as GeantV, ROOT, and experiments' frameworks. Nevertheless, fully exploiting SIMD parallelism will be a required step in order to bridge the widening gap between the needs and availability of computing resouces for data analysis and processing in particle physics. Although there are SIMD libraries that wrap compiler intrinsics into a convenient interface, they do not always support all available architectures, or they only perform well in some of them. The VecCore library was created to address some of these performance and portability issues by providing a unified abstraction layer on top of existing libraries, such as Vc or UME::SIMD. In this article, we discuss VecCore's programming model for SIMD code and some use cases in HEP software packages such as VecGeom and GeantV.

Eelgrass coverage in Odense Fjord (Denmark) has declined by 90% since 1983, due to eutrophication and its associated pressures, and the state of low eelgrass coverage has remained stable despite 10 to 15 yr of reduced nutrient loading and improved water quality. We hypothesize that the survival of eelgrass seedlings, and thus recolonization through reproductive dispersal, is negatively affected by physical disturbances. The 3 most likely physical mechanisms involved are uprooting or burial through drifting macroalgae, Arenicola marina sediment reworking and current-driven sediment resuspension. Our hypothesis was tested by field observations during the summer of 2009, when the mortality of seedlings was followed through time. The density of seedlings decreased dramatically by 80% during the first month of observations, and no seedlings survived past August, corresponding to an average seedling mortality of 1.5% d super(-1). This was >3 times higher than the mortality for seedlings protected from physical disturbance by enclosures (0.4% d super(-1)), indicating that physical disturbance contributed to high seedling mortality. A significant correlation (p = 0.02) between macroalgal drift and seedling mortality suggested that ~40% of seedlings were lost due to the physical disturbance of drifting algae. In contrast, no correlations were found between A. marina reworking or resuspension and seedling mortality, despite a mobility of up to 400 cm super(3) sediment m super(-2) d super(-1) by these mechanisms. Given the observed intensity of macroalgal drift, we speculate that this mechanism severely hampers eelgrass reestablishment in certain parts of Odense Fjord.

Efficient random number generation with high quality statistical properties and exact reproducibility of Monte Carlo simulations are important requirements in many areas of computational science. VecRNG is a package providing pseudo-random number generation (pRNG) in the context of a new library VecMath. This library bundles up several general-purpose mathematical utilities, data structures, and algorithms having both SIMD and SIMT (GPUs) support based on VecCore. Several state-of-the-art RNG algorithms are implemented as kernels supporting parallel generation of random numbers in scalar, vector, and Cuda workflows. In this report, we will present design considerations, implementation details, and computing performance of parallel pRNG engines on both CPU and GPU. Reproducibility of propagating multiple particles in parallel for HEP event simulation is demonstrated, using GeantV-based examples, for both sequential and fine-grain track-level concurrent simulation workflows. Strategies for efficient uses of vectorized pRNG and non-overlapping streams of random number sequences in concurrent computing environments is discussed as well.

Previous studies suggest that current-driven plant transport in shallow lagoons and estuaries is associated with increased turbidity. Our hypothesis is therefore that macroalgae erode surface sediment while drifting as bedload. This ballistic effect of moving plants on surface sediment was tested in a series of controlled annular flume experiments, where simultaneous measurements of macrophytes transport and turbidity were conducted at increasing current velocities. Sediment erosion always started earlier in experiments with plants than in control experiments without plants. Turbidity increased immediately when plants started to move at current velocities of 2-4 cm s⁻¹. From a background concentration of 7-10 mg SPM l⁻¹, turbidity increased to 30-50 mg SPM l⁻¹ for Ceramium sp., Ulva lactuca and Chaetomorpha linum, while the more rigid Gracilaria sp., caused much higher turbidities (50-180 mg SPM l⁻¹). Such plant induced sediment erosion at low current velocity can explain the observed appearance of turbid waters in estuaries and lagoons in the absence of strong wind and wave action. Based on 3-D hydrodynamic modelling, it was determined that plant driven erosion occurs during most of the growth season in a shallow eutrophic estuary (Odense Fjord, Denmark).

When processing large amounts of data, the rate at which reading and writing can take place is a critical factor. High energy physics data processing relying on ROOT is no exception. The recent parallelisation of LHC experiments' software frameworks and the analysis of the ever increasing amount of collision data collected by experiments further emphasised this issue underlying the need of increasing the implicit parallelism expressed within the ROOT I/O. In this contribution we highlight the improvements of the ROOT I/O subsystem which targeted a satisfactory scaling behaviour in a multithreaded context. The effect of parallelism on the individual steps which are chained by ROOT to read and write data, namely (de)compression, (de)serialisation, access to storage backend, are discussed. Performance measurements are discussed through real life examples coming from CMS production workflows on traditional server platforms and highly parallel architectures such as Intel Xeon Phi.

The bright future of particle physics at the Energy and Intensity frontiers poses exciting challenges to the scientific software community. The traditional strategies for processing and analysing data are evolving in order to (i) offer higher-level programming model approaches and (ii) exploit parallelism to cope with the ever increasing complexity and size of the datasets. This contribution describes how the ROOT framework, a cornerstone of software stacks dedicated to particle physics, is preparing to provide adequate solutions for the analysis of large amount of scientific data on parallel architectures. The functional approach to parallel data analysis provided with the ROOT TDataFrame interface is then characterised. The design choices behind this new interface are described also comparing with other widely adopted tools such as Pandas and Apache Spark. The programming model is illustrated highlighting the reduction of boilerplate code, composability of the actions and data transformations as well as the capabilities of dealing with different data sources such as ROOT, JSON, CSV or databases. Details are given about how the functional approach allows transparent implicit parallelisation of the chain of operations specified by the user. The progress done in the field of distributed analysis is examined. In particular, the power of the integration of ROOT with Apache Spark via the PyROOT interface is shown. In addition, the building blocks for the expression of parallelism in ROOT are briefly characterised together with the structural changes applied in the building and testing infrastructure which were necessary to put them in production.

The need for processing the ever-increasing amount of data generated by the LHC experiments in a more efficient way has motivated ROOT to further develop its support for parallelism. Such support is being tackled both for shared-memory and distributed-memory environments. The incarnations of the aforementioned parallelism are multi-threading, multi-processing and cluster-wide executions. In the area of multi-threading, we discuss the new implicit parallelism and related interfaces, as well as the new building blocks to safely operate with ROOT objects in a multi-threaded environment. Regarding multi-processing, we review the new MultiProc framework, comparing it with similar tools (e.g. multiprocessing module in Python). Finally, as an alternative to PROOF for cluster-wide executions, we introduce the efforts on integrating ROOT with state-of-the-art distributed data processing technologies like Spark, both in terms of programming model and runtime design (with EOS as one of the main components). For all the levels of parallelism, we discuss, based on real-life examples and measurements, how our proposals can increase the productivity of scientists.

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.

Preclinical studies have demonstrated that the chemotherapeutic action of oxaliplatin, a third generation platinum derivative, is improved when combined with cetuximab, a monoclonal antibody inhibitor of epidermal growth factor receptors. To explore the mechanism of this synergistic benefit, we used HCT-8 and HCT-116, two human colon cancer cell lines, respectively, responsive and non-responsive to the oxaliplatin/cetuximab combination. We examined the effect of drug exposure on glutathione- S -transferase-mediated oxaliplatin detoxification, DNA–platinum adducts formation, cell cycle distribution, apoptosis, and the expression of multiple targets involved in DNA replication, recombination, and repair. The major changes we found in HCT-8 were a stimulation of oxaliplatin–DNA adduct formation associated with reduced expression of the key enzyme (excision repair cross complementation group1: ERCC1 ) in the key repair process of oxaliplatin–DNA platinum adduct, the nucleotide excision repair (NER), both at the mRNA and protein levels. We also observed a reduced expression of factors involved in DNA replication initiation, which correlates with an enrichment of cells in the G1 phase of the cell cycle as well as an acceleration of apoptosis. None of these changes occurred in the non-responsive HCT-116 cell that we used as a negative control. These findings support the fact that cetuximab potentiates the oxaliplatin-mediated cytotoxic effect as the result of inhibition of NER and also DNA replication initiation.

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.