We present a detailed overview of the cosmological surveys that we aim to carry out with Phase 1 of the Square Kilometre Array (SKA1) and the science that they will enable. We highlight three main surveys: a medium-deep continuum weak lensing and low-redshift spectroscopic HI galaxy survey over 5 000 deg2; a wide and deep continuum galaxy and HI intensity mapping (IM) survey over 20 000 deg2 from $z = 0.35$ to 3; and a deep, high-redshift HI IM survey over 100 deg2 from $z = 3$ to 6. Taken together, these surveys will achieve an array of important scientific goals: measuring the equation of state of dark energy out to $z \sim 3$ with percent-level precision measurements of the cosmic expansion rate; constraining possible deviations from General Relativity on cosmological scales by measuring the growth rate of structure through multiple independent methods; mapping the structure of the Universe on the largest accessible scales, thus constraining fundamental properties such as isotropy, homogeneity, and non-Gaussianity; and measuring the HI density and bias out to $z = 6$ . These surveys will also provide highly complementary clustering and weak lensing measurements that have independent systematic uncertainties to those of optical and near-infrared (NIR) surveys like Euclid, LSST, and WFIRST leading to a multitude of synergies that can improve constraints significantly beyond what optical or radio surveys can achieve on their own. This document, the 2018 Red Book, provides reference technical specifications, cosmological parameter forecasts, and an overview of relevant systematic effects for the three key surveys and will be regularly updated by the Cosmology Science Working Group in the run up to start of operations and the Key Science Programme of SKA1.

The scientific community is presently witnessing an unprecedented growth in the quality and quantity of data sets coming from simulations and real-world experiments. To access effectively and extract the scientific content of such large-scale data sets (often sizes are measured in hundreds or even millions of Gigabytes) appropriate tools are needed. Visual data exploration and discovery is a robust approach for rapidly and intuitively inspecting large-scale data sets, e.g. for identifying new features and patterns or isolating small regions of interest within which to apply time-consuming algorithms. This paper presents a high performance parallelized implementation of Splotch, our previously developed visual data exploration and discovery algorithm for large-scale astrophysical data sets coming from particle-based simulations. Splotch has been improved in order to exploit modern massively parallel architectures, e.g. multicore CPUs and CUDA-enabled GPUs. We present performance and scalability benchmarks on a number of test cases, demonstrating the ability of our high performance parallelized Splotch to handle efficiently large-scale data sets, such as the outputs of the Millennium II simulation, the largest cosmological simulation ever performed.

This paper extends the method introduced in Rivi et al. (2016b) to measure galaxy ellipticities in the visibility domain for radio weak lensing surveys. In that paper we focused on the development and testing of the method for the simple case of individual galaxies located at the phase centre, and proposed to extend it to the realistic case of many sources in the field of view by isolating visibilities of each source with a faceting technique. In this second paper we present a detailed algorithm for source extraction in the visibility domain and show its effectiveness as a function of the source number density by running simulations of SKA1-MID observations in the band 950-1150 MHz and comparing original and measured values of galaxies' ellipticities. Shear measurements from a realistic population of 10 4 galaxies randomly located in a field of view of 1 deg 2 (i.e. the source density expected for the current radio weak lensing survey proposal with SKA1) are also performed. At SNR 10, the multiplicative bias is only a factor 1.5 worse than what found when analysing individual sources, and is still comparable to the bias values reported for similar measurement methods at optical wavelengths. The additive bias is unchanged from the case of individual sources, but it is significantly larger than typically found in optical surveys. This bias depends on the shape of the uv coverage and we suggest that a uv-plane weighting scheme to produce a more isotropic shape could reduce and control additive bias.

Radio weak lensing, while a highly promising complementary probe to optical weak lensing, will require incredible precision in the measurement of galaxy shape parameters. In this paper, we extend the Bayesian Inference for Radio Observations model fitting approach to measure galaxy shapes directly from visibility data of radio continuum surveys, instead of from image data. We apply a Hamiltonian Monte Carlo (HMC) technique for sampling the posterior, which is more efficient than the standard Monte Carlo Markov Chain method when dealing with a large dimensional parameter space. Adopting the exponential profile for galaxy model fitting allows us to analytically calculate the likelihood gradient required by HMC, allowing a faster and more accurate sampling. The method is tested on SKA1-MID simulated observations at 1.4 GHz of a field containing up to 1000 star-forming galaxies. It is also applied to a simulated observation of the weak lensing precursor survey SuperCLASS. In both cases we obtain reliable measurements of the galaxies' ellipticity and size for all sources with SNR 10, and we also find relationships between the convergence properties of the HMC technique and some source parameters. Direct shape measurement in the visibility domain achieves high accuracy at the expected source number densities of the current and next SKA precursor continuum surveys. The proposed method can be easily extended for the fitting of other galaxy and scientific parameters, as well as simultaneously marginalising over systematic and instrumental effects.

Splotch is a rendering algorithm for exploration and visual discovery in particlebased datasets coming from astronomical observations or numerical simulations. The strengths of the approach are production of high quality imagery and support for very large-scale datasets through an effective mix of the OpenMP and MPI parallel programming paradigms. This article reports our experiences in re-designing Splotch for exploiting emerging HPC architectures nowadays increasingly populated with GPUs. A performance model is introduced to guide our re-factoring of Splotch. A number of parallelization issues are discussed, in particular relating to race conditions and workload balancing, towards achieving optimal performances. Our implementation was accomplished by using the CUDA programming paradigm. Our strategy is founded on novel schemes achieving optimised data organisation and classification of particles. We deploy a reference cosmological simulation to present performance results on acceleration gains and scalability. We finally outline our vision for future work developments including possibilities for further optimisations and exploitation of hybrid systems and emerging accelerators.

The high sensitivity of the new generation of radio telescopes such as the Square Kilometre Array (SKA) will allow cosmological weak lensing measurements at radio wavelengths that are competitive with optical surveys. We present an adaptation to radio data of lensfit, a method for galaxy shape measurement originally developed and used for optical weak lensing surveys. This likelihood method uses an analytical galaxy model and makes a Bayesian marginalisation of the likelihood over uninteresting parameters. It has the feature of working directly in the visibility domain, which is the natural approach to adopt with radio interferometer data, avoiding systematics introduced by the imaging process. As a proof of concept, we provide results for visibility simulations of individual galaxies with flux density S 10µJy at the phase centre of the proposed SKA1-MID baseline configuration, adopting 12 frequency channels in the band 950 -1190 MHz. Weak lensing shear measurements from a population of galaxies with realistic flux and scalelength distributions are obtained after natural gridding of the raw visibilities. Shear measurements are expected to be affected by 'noise bias': we estimate the bias in the method as a function of signal-to-noise ratio (SNR). We obtain additive and multiplicative bias values that are comparable to SKA1 requirements for SNR > 18 and SNR > 30, respectively. The multiplicative bias for SNR > 10 is comparable to that found in ground-based optical surveys such as CFHTLenS, and we anticipate that similar shear measurement calibration strategies to those used for optical surveys may be used to good effect in the analysis of SKA radio interferometer data.

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Optimization of water distribution networks is a NP-hard problem that researchers have tried to deal with using different formulations and algorithmic approaches. Among these, multi-objective heuristic algorithms are interesting because of their capacity for dealing with separate objectives that allow us to choose a posteriori the best compromise, but one of their main drawbacks is the long time required to obtain good solutions. Parallel processing is the most promising way to reduce the computing time and can make the convergence to adequate solutions faster. This paper intends to