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The analysis of scientific data, specially in different kinds of cosmological studies, has to deal with the increment in data volume. These studies include the calculation of correlation functions such as the Two-point Three-Dimensional Correlation Function. To get the final estimator value for these functions, it is necessary to construct histograms for storing large number counts. Histograms are a very common way of representing data and summarizing information in science. However, they have a high computational cost, which is worsened by the increase of the standard sample size. This increment leads directly to two problems: first of all, the large processing time and, secondly, the lack of accuracy of the result. Therefore, the implementations of correlation functions need to maintain high accuracy and affordable processing times. In order to reduce the high processing times, GPU computing is being widely used. In this work, the bin-recycling strategy is implemented and evaluated in the Two-Point Three-Dimensional Correlation Function. We show that this implementation outperforms others which also correctly process a large number of galaxies. As a result of this work, an accuracy-aware implementation of the Two-Point Three-Dimensional Correlation Function on GPU is described and evaluated to ensure the correctness of the results.

Histogram calculation is an essential part of many scientific analyses. In Cosmology, histograms are employed intensively in the computation of correlation functions of galaxies, as part of Large Scale Structure studies. Among the most commonly used ones are the two-point, three-point and the shear–shear correlation functions. In these computations, the precision of the calculation of the counts in each bin is a key element for achieving the highest accuracy. In order to accelerate the analysis of increasingly larger datasets, GPU computing is becoming widely employed in this field. However, the recommended histogram calculation procedure becomes less precise when bins become highly populated in these sort of algorithms. In this work, an alternative implementation to correct this problem is proposed and tested. This approach is based on distributing the creation of histograms between the CPU and GPU. The implementation is tested using three cosmological analyses with observational data. The results show an increased performance in terms of accuracy while keeping the same execution time.

Light rays are deflected when travelling through a gravitational potential: this phenomenon is known as gravitational lensing. It causes the observed shapes of distant galaxies to be very slightly distorted by the intervening matter in the Universe, as their light travels towards us. This distortion is called cosmic shear . By measuring this component it is possible to derive the properties of the mass distribution causing the distortion. This in turn can lead to the measurement of the accelerated expansion of the Universe, as matter clumps together differently depending on its dynamics at each cosmological epoch. The measurement of the cosmic shear requires the statistical analysis of the ellipticities of millions of galaxies using very large astronomical surveys. In the past, due to the computational cost of the problem, this kind of analysis was performed by introducing simplifications in the estimation of such statistics. With the advent of scientific computing using graphics processing units, analysis of the shear can be addressed without approximations, even for very large surveys, while maintaining an affordable execution time. In this work, we present the creation and optimization of such a graphics processing unit code to compute the so-called shear–shear correlation function.

Cosmology is a data intensive field that will become more so in the coming years. In this scenario, GPU-computing is becoming very relevant for analysing large catalogs within a reasonable amount of time. In this work, we investigate the acceleration of the analysis by using a hybrid MPI-CUDA implementation.

Measurements of the weak electric dipole moment dτω and, for the first time, the weak magnetic dipole moment aτω of the τ lepton using L3 detector at LEP are presented. Azimuthal asymmetries for τ → πν and τ → ϱν are used to obtain these measurements. Observed asymmetries are consistent with zero, and the limits set on dτω and aτω are |dτω

Nowadays many computational systems are endowed of multi-cores in the main processor units, and one or more many-core cards. This makes possible the execution of codes on both computational resources concurrently. The challenge in this scenario is to balance correctly both execution paths. When the scenario is simple enough, by-hand optimization can be affordable, otherwise metaheuristic techniques are mandatory. In this work, Differential Evolution algorithm is implemented to optimize a concurrent CPU-GPU code calculating the Two-Point Angular Correlation Function applied to the study of Large-Scale Structure of the Universe. The Two-Point Angular Correlation Function is a computationally intensive function, requiring the calculation of three histograms with different execution times. Therefore, this forces to implement a parameter for describing the percentage of computation in CPU per histogram, and the counterpart in GPU; and to use metaheuristic techniques to fit the appropriate values for these three percentages. As a consequence of the optimization process described in this article, a significant reduction of the execution time is achieved. This proof of concept demonstrates that Evolutionary Algorithms are useful for fairly balancing computational paths in concurrent computing scenarios.