Vortex methods for incompressible flow simulations on the GPU

We present a remeshed vortex particle method for incompressible flow simulations on GPUs. The particles are convected in a Lagrangian frame and are periodically reinitialized on a regular grid. The grid is used in addition to solve for the velocity-vorticity Poisson equation and for the computation of the diffusion operators. In the present GPU implementation of particle methods, the remeshing and the solution of the Poisson equation rely on fast and efficient mesh-particle interpolations. We demonstrate that particle remeshing introduces minimal artificial dissipation, enables a faster computation of differential operators on particles over grid-free techniques and can be efficiently implemented on GPUs. The results demonstrate that, contrary to common practice in particle simulations, it is necessary to remesh the (vortex) particle locations in order to solve accurately the equations they discretize, without compromising the speed of the method. The present method leads to simulations of incompressible vortical flows on GPUs with unprecedented accuracy and efficienc

Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney

The performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks

Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney

The performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks

Large-scale morphometry of the subarachnoid space of the optic nerve.

BACKGROUND The meninges, formed by dura, arachnoid and pia mater, cover the central nervous system and provide important barrier functions. Located between arachnoid and pia mater, the cerebrospinal fluid (CSF)-filled subarachnoid space (SAS) features a variety of trabeculae, septae and pillars. Like the arachnoid and the pia mater, these structures are covered with leptomeningeal or meningothelial cells (MECs) that form a barrier between CSF and the parenchyma of the optic nerve (ON). MECs contribute to the CSF proteome through extensive protein secretion. In vitro, they were shown to phagocytose potentially toxic proteins, such as α-synuclein and amyloid beta, as well as apoptotic cell bodies. They therefore may contribute to CSF homeostasis in the SAS as a functional exchange surface. Determining the total area of the SAS covered by these cells that are in direct contact with CSF is thus important for estimating their potential contribution to CSF homeostasis. METHODS Using synchrotron radiation-based micro-computed tomography (SRµCT), two 0.75 mm-thick sections of a human optic nerve were acquired at a resolution of 0.325 µm/pixel, producing images of multiple terabytes capturing the geometrical details of the CSF space. Special-purpose supercomputing techniques were employed to obtain a pixel-accurate morphometric description of the trabeculae and estimate internal volume and surface area of the ON SAS. RESULTS In the bulbar segment, the ON SAS microstructure is shown to amplify the MECs surface area up to 4.85-fold compared to an "empty" ON SAS, while just occupying 35% of the volume. In the intraorbital segment, the microstructure occupies 35% of the volume and amplifies the ON SAS area 3.24-fold. CONCLUSIONS We provided for the first time an estimation of the interface area between CSF and MECs. This area is of importance for estimating a potential contribution of MECs on CSF homeostasis

AN5D: Automated Stencil Framework for High-Degree Temporal Blocking on GPUs

Stencil computation is one of the most widely-used compute patterns in high performance computing applications. Spatial and temporal blocking have been proposed to overcome the memory-bound nature of this type of computation by moving memory pressure from external memory to on-chip memory on GPUs. However, correctly implementing those optimizations while considering the complexity of the architecture and memory hierarchy of GPUs to achieve high performance is difficult. We propose AN5D, an automated stencil framework which is capable of automatically transforming and optimizing stencil patterns in a given C source code, and generating corresponding CUDA code. Parameter tuning in our framework is guided by our performance model. Our novel optimization strategy reduces shared memory and register pressure in comparison to existing implementations, allowing performance scaling up to a temporal blocking degree of 10. We achieve the highest performance reported so far for all evaluated stencil benchmarks on the state-of-the-art Tesla V100 GPU

Vortex methods for incompressible flow simulations on the GPU

ISSN:0178-2789ISSN:1432-231

Effect of the extension of maternity leave from 90 to 98 days on early childhood development in Peru for the 2016 – 2019 period

El presente estudio evalúa los efectos de la reforma referida a la ampliación de la licencia de maternidad, de 90 a 98 días, implementada en Perú en el año 2016 sobre los siete resultados identificados como parte de los Lineamientos “Primero la Infancia”, asociados a distintos momentos del desarrollo de niños y niñas. Para ello, empleamos información proveniente de la Encuesta Demográfica y de Salud Familiar (ENDES) para los años 2015 a 2019 y aplicamos un modelo de regresión discontinua, considerando el estimador de intención a tratar y filtros que permitan aproximarnos a una muestra de mujeres elegibles para recibir este beneficio, tal que los niños y niñas que nacieron luego de la fecha de implementación de la ley son considerados como beneficiarios (grupo de tratamiento) y quienes nacieron antes no (grupo de control). Los resultados revelan que una mayor duración de la licencia de maternidad contribuye positivamente en el peso al nacer de niños y niñas (+0.09/+0.1kg), en su estado nutricional (desnutrición OMS: -4/-5 pp.) y en la capacidad que tienen de representar y evocar hechos u objetos que no están presentes (+8/+12 pp.). Este trabajo contribuye con la literatura existente en materia de políticas de protección de la maternidad y de inversión en la primera infancia al ser el primero en analizar los efectos de la ampliación de la licencia de maternidad en la primera infancia en el Perú.This study evaluates the effects of the reform referred to the extension of the maternity leave, from 90 to 98 days, implemented in Peru in 2016 on the seven results identified as part of the “Primero la Infancia” Guidelines, associated with different moments of the child’s development. To do this, we used information from the Demographic and Family Health Survey (ENDES) for the years 2015 to 2019 and applied a regression discontinuity approach, considering the intent-to-treat estimator and filters that approximated our sample to an eligible group of women for this benefit, such that children who were born after the implementation date of the law are considered as beneficiaries (treatment group) and those who were born before are not (control group). The results reveal that a longer duration of maternity leave contributes positively to the birth weight of newborns (+0.09/+0.1kg), the child nutritional status (malnutrition WHO: -4/-5 pp.) and their ability to represent and evoke events or objects that are not present (+8/+12 pp.). This work contributes to the existing literature on maternity protection and investment policies in early childhood by being the first to analyze the effects of the maternity leave extension policy on early childhood outcomes in Peru.Tesi

Flow simulations using particles - Bridging Computer Graphics and CFD

International audienceThe simulation of fluid flows using particles is becoming increasingly popular in Computer Graphics (CG). The grid-free character of particles, the flexibility in handling complex flow configurations and the possibility to obtain visually realistic results with a small number of computational elements are some of the main reasons for the success of these methods. In the Computational Fluid Dynamics (CFD) community, the realization that by periodically regularizing the particle locations can lead to highly accurate flow simulations, without detracting from the adaptivity and robustness of the method has led in turn to a renaissance in flow simulations using particles. In this course we review recent advances in flow simulations using particles with a focus on developing a bridge fostering an interdisciplinary scientific exchange between the CG and the CFD communities. The course will describe advances in particle methods in a comparative, case study driven framework. In this framework we will address for example visual realism of liquid simulations as related to the accuracy of enforcing incompressibility constraints in Smooth Particle Hydrodynamics (SPH) and Vortex Methods (VM). We will discuss the role of advantages and drawbacks for particle simulations when using remeshing, we will present techniques for the effective handling of fluids interacting with solids and free surfaces and in turn the use of Computer Graphics algorithms and hardware to accelerate flow simulations of relevance to the CFD community