Fast Collision Culling in Large-Scale Environments Using GPU Mapping Function

International audienceThis paper presents a novel and efficient GPU-based parallel algorithm to cull non-colliding object pairs in very large-scale dynamic simulations. It allows to cull objects in less than 25ms with more than 100K objects. It is designed for many-core GPU and fully exploits multi-threaded capabilities and data-parallelism. In order to take advantage of the high number of cores, a new mapping function is defined that enables GPU threads to determine the objects pair to compute without any global memory access. These new optimized GPU kernel functions use the thread indexes and turn them into a unique pair of objects to test. A square root approximation technique is used based on Newton's estimation, enabling the threads to only perform a few atomic operations. A first characterization of the approximation errors is presented, enabling the fixing of incorrect computations. The I/O GPU streams are optimized using binary masks. The implementation and evaluation is made on largescale dynamic rigid body simulations. The increase in speed is highlighted over other recently proposed CPU and GPU-based techniques. The comparison shows that our system is, in most cases, faster than previous approaches

A Broad Phase Collision Detection Algorithm Adapted to Multi-cores Architectures

International audienceRecent years have seen the impressive evolution of graphics hardware and processors architecture from single core to multi and many-core architectures. Confronted to this evolution, new trends in collision detection optimisation consist in proposing a solution that maps on the runtime architecture. We present, in this paper, two contributions in the field of collision detection in large-scale environments. We present a first way to parallelise, on a multi-core architecture, the initial step of the collision detection pipeline: the broad-phase. Then, we describe a new formalism of the collision detection pipeline that takes into account runtime architecture. The well-known broadphase algorithm used is the ”Sweep and Prune” and it has been adapted to a multi-threading use. To handle one or more thread per core, critical writing sections and threads idling must be minimised. Our model is able to work on a n-core architecture reducing computation time to detect collision between 3D objects in a large-scale environment

FaceTuneGAN: Face Autoencoder for Convolutional Expression Transfer Using Neural Generative Adversarial Networks

In this paper, we present FaceTuneGAN, a new 3D face model representation decomposing and encoding separately facial identity and facial expression. We propose a first adaptation of image-toimage translation networks, that have successfully been used in the 2D domain, to 3D face geometry. Leveraging recently released large face scan databases, a neural network has been trained to decouple factors of variations with a better knowledge of the face, enabling facial expressions transfer and neutralization of expressive faces. Specifically, we design an adversarial architecture adapting the base architecture of FUNIT and using SpiralNet++ for our convolutional and sampling operations. Using two publicly available datasets (FaceScape and CoMA), FaceTuneGAN has a better identity decomposition and face neutralization than state-of-the-art techniques. It also outperforms classical deformation transfer approach by predicting blendshapes closer to ground-truth data and with less of undesired artifacts due to too different facial morphologies between source and target

Gonadotropin administration to mimic mini-puberty in hypogonadotropic males: pump or injections?

Objective: Newborns with congenital hypogonadotropic hypogonadism (CHH) have an impaired postnatal activation of the gonadotropic axis. Substitutive therapy with recombinant gonadotropins can be proposed to mimic physiological male mini-puberty during the first months of life. The aim of this study was to co mpare the clinical and biological efficacy of two treatment modalities of gonadotropins administration during mini-puberty in CHH neonates. Design: Multicenter retrospective analytical epidemiological study comparing two treatments, pump vs injection, between 2004 and 2019. Methods: Clinical (penile size, testis size, testicular descent) and biological parameters (serum concentrations of testosterone, anti-Müllerian hormone (AMH) and Inhibin B) were compared between the two groups by multivariate analyses. Results: Thirty-five patients were included. A significantly higher incre ase in penile length and testosterone level was observed in the injection group compared to the pump group (+0.16 ± 0.02 mm vs +0.10 ± 0.02 mm per day, P = 0.002; and +0.04 ± 0.007 ng/mL vs +0.01 ± 0.008 ng/mL per day, P = 0.001). In both groups, significant increases in penile length and width, testosterone, AMH, and Inhibin B levels were observed, as well as improved testicular descent (odds ratio of not being in a scrotal position at the end of treatment = 0.97 (0.96; 0.99)). Conclusions: Early postnatal administration of recombinant gonadotropins in CHH boys is effective in stimulating penile growth, Sertoli cell proliferati on, and testicular descent, with both treatment modalities

A roadmap for research in post-stroke fatigue:Consensus-based core recommendations from the third Stroke Recovery and Rehabilitation Roundtable

Rationale: Fatigue affects almost half of all people living with stroke. Stroke survivors rank understanding fatigue and how to reduce it as one of the highest research priorities. Methods: We convened an interdisciplinary, international group of clinical and pre-clinical researchers and lived experience experts. We identified four priority areas: (1) best measurement tools for research, (2) clinical identification of fatigue and potentially modifiable causes, (3) promising interventions and recommendations for future trials, and (4) possible biological mechanisms of fatigue. Cross-cutting themes were aphasia and the voice of people with lived experience. Working parties were formed and structured consensus building processes were followed. Results: We present 20 recommendations covering outcome measures for research, development, and testing of new interventions and priority areas for future research on the biology of post-stroke fatigue. We developed and recommend the use of the Stroke Fatigue Clinical Assessment Tool. Conclusions: By synthesizing current knowledge in post-stroke fatigue across clinical and pre-clinical fields, our work provides a roadmap for future research into post-stroke fatigue

A roadmap for research in post-stroke fatigue: consensus-based core recommendations from the third Stroke Recovery and Rehabilitation Roundtable

Rationale: Fatigue affects almost half of all people living with stroke. Stroke survivors rank understanding fatigue and how to reduce it as one of the highest research priorities. Methods: We convened an interdisciplinary, international group of clinical and pre-clinical researchers and lived experience experts. We identified four priority areas: (1) best measurement tools for research, (2) clinical identification of fatigue and potentially modifiable causes, (3) promising interventions and recommendations for future trials, and (4) possible biological mechanisms of fatigue. Cross-cutting themes were aphasia and the voice of people with lived experience. Working parties were formed and structured consensus building processes were followed. Results: We present 20 recommendations covering outcome measures for research, development, and testing of new interventions and priority areas for future research on the biology of post-stroke fatigue. We developed and recommend the use of the Stroke Fatigue Clinical Assessment Tool. Conclusions: By synthesizing current knowledge in post-stroke fatigue across clinical and pre-clinical fields, our work provides a roadmap for future research into post-stroke fatigue

Détection de Collision pour Environnements Large Échelle : Modèle Unifié et Adaptatif pour Architectures Multi-coeur et Multi-GPU

Virtual reality environments are becoming increasingly large and complex and real-time interaction level is becoming difficult to stably insure. Indeed, because of their complexity, detailed geometry and specific physical properties, these large scale environments create a critical computational bottleneck on physical algorithms. Our work focused on the first step of the physical process: the collision detection. These algorithms can sometimes have a quadratic complexity. Solving and simplifying the collision detection problem is integral to alleviating this bottleneck. Hardware architectures have undergone extensive changes in the last few years that have opened new ways to relieve this computational bottleneck. Multiple processor cores offer the ability to execute algorithms in parallel on one single processor. At the same time, graphics cards have gone from being a simple graphical display device to a supercomputer. These supercomputers now enjoy attention from a specialized community dealing solely with physical simulation. To perform large scale simulations and remain generic on the runtime architecture, we proposed unified and adaptive mapping models between collision detection algorithms and the runtime architecture using multi-core and multi-GPU architectures. We have developed innovative and effective solutions to significantly reduce the computation time in large scale environments while ensuring the stability and reproducibility of results. Our models cover the global collision detection pipeline, focusing both high and low level algorithms. Our models based on multi-core, GPU and multi-GPU combine different techniques of spatial subdivision algorithms based on topology and load balancing techniques based on data stealing. Our hybrid solution enables us to increase space and computing time within large scale virtual environments. The coupling of these new algorithms led us to develop two models of dynamic algorithmic adaptation based (or not) on off-line precomputed scenarios. Finally, it became necessary to add a new dimension to the collision detection pipeline taking into account of the architecture for optimal execution. With this formalism, we proposed a new pipeline of collision detection with a granularity of parallelism on multicore processors or multi-GPU platforms. It enables simultaneous execution of different stages of the pipeline and a parallel internal to each of these steps.Les environnements de réalité virtuelle devenant de plus en plus complexes et de très grandes dimensions, un niveau d'interaction temps-réel devient impossible à garantir. En effet, de par leur complexité, due à une géométrie détaillée et aux propriétés physiques spécifiques, ces environnements large échelle engendrent un goulet d'étranglement calculatoire critique sur les algorithmes de simulation physique. Nous avons focalisé nos travaux sur la première étape de ces algorithmes qui concerne la détection de collision, car les problématiques font partie intégrante de ce goulet d'étranglement et leur complexité peut parfois se révéler quadratique dans certaines situations. Le profond bouleversement que subissent les architectures machines depuis quelques années ouvre une nouvelle voie pour réduire le goulet d'étranglement. La multiplication du nombre de cœurs offre ainsi la possibilité d'exécuter ces algorithmes en parallèle sur un même processeur. Dans le même temps, les cartes graphiques sont passées d'un statut de simple périphérique d'affichage graphique à celui de supercalculateur. Elles jouissent désormais d'une attention toute particulière de la part de la communauté traitant de la simulation physique. Afin de passer au large échelle et d'être générique sur la machine d'exécution, nous avons proposé des modèles unifiés et adaptatifs de correspondance entre les algorithmes de détection de collision et les architectures machines de type multi-coeur et multi-GPU. Nous avons ainsi défini des solutions innovantes et performantes permettant de réduire significativement le temps de calcul au sein d'environnements large échelle tout en assurant la pérennité des résultats. Nos modèles couvrent l'intégralité du pipeline de détection de collision en se focalisant aussi bien sur des algorithmes de bas ou de haut niveau. Nos modèles multi-coeur, GPU et multi-GPU allient différentes techniques de subdivision spatiale à des algorithmes basés topologie ainsi que des techniques d'équilibrage de charge basées sur le vol de données. Notre solution hybride permet d'accroitre l'espace et le temps de calcul ainsi que le passage au large échelle. L'association de ces nouveaux algorithmes nous a permis de concevoir deux modèles d'adaptation algorithmique dynamique basés, ou non, sur des scénarios de pré-calcul hors-ligne. Enfin, il nous est apparu indispensable d'ajouter au pipeline de détection de collision une nouvelle dimension révélant la prise en compte des architectures pour une exécution optimale. Grâce à ce formalisme, nous avons proposé un nouveau pipeline de détection de collision offrant une granularité de parallélisme sur processeurs multi-coeur. Il permet une exécution simultanée des différentes étapes du pipeline ainsi qu'un parallélisme interne à chacune de ces étapes

Détection de Collision pour Environnements Large Échelle : Modèle Unifié et Adaptatif pour Architectures Multi-coeur et Multi-GPU

Virtual reality environments are becoming increasingly large and complex and real-time interaction level is becoming difficult to stably insure. Indeed, because of their complexity, detailed geometry and specific physical properties, these large scale environments create a critical computational bottleneck on physical algorithms. Our work focused on the first step of the physical process: the collision detection. These algorithms can sometimes have a quadratic complexity. Solving and simplifying the collision detection problem is integral to alleviating this bottleneck. Hardware architectures have undergone extensive changes in the last few years that have opened new ways to relieve this computational bottleneck. Multiple processor cores offer the ability to execute algorithms in parallel on one single processor. At the same time, graphics cards have gone from being a simple graphical display device to a supercomputer. These supercomputers now enjoy attention from a specialized community dealing solely with physical simulation. To perform large scale simulations and remain generic on the runtime architecture, we proposed unified and adaptive mapping models between collision detection algorithms and the runtime architecture using multi-core and multi-GPU architectures. We have developed innovative and effective solutions to significantly reduce the computation time in large scale environments while ensuring the stability and reproducibility of results. Our models cover the global collision detection pipeline, focusing both high and low level algorithms. Our models based on multi-core, GPU and multi-GPU combine different techniques of spatial subdivision algorithms based on topology and load balancing techniques based on data stealing. Our hybrid solution enables us to increase space and computing time within large scale virtual environments. The coupling of these new algorithms led us to develop two models of dynamic algorithmic adaptation based (or not) on off-line precomputed scenarios. Finally, it became necessary to add a new dimension to the collision detection pipeline taking into account of the architecture for optimal execution. With this formalism, we proposed a new pipeline of collision detection with a granularity of parallelism on multicore processors or multi-GPU platforms. It enables simultaneous execution of different stages of the pipeline and a parallel internal to each of these steps.Les environnements de réalité virtuelle devenant de plus en plus complexes et de très grandes dimensions, un niveau d'interaction temps-réel devient impossible à garantir. En effet, de par leur complexité, due à une géométrie détaillée et aux propriétés physiques spécifiques, ces environnements large échelle engendrent un goulet d'étranglement calculatoire critique sur les algorithmes de simulation physique. Nous avons focalisé nos travaux sur la première étape de ces algorithmes qui concerne la détection de collision, car les problématiques font partie intégrante de ce goulet d'étranglement et leur complexité peut parfois se révéler quadratique dans certaines situations. Le profond bouleversement que subissent les architectures machines depuis quelques années ouvre une nouvelle voie pour réduire le goulet d'étranglement. La multiplication du nombre de cœurs offre ainsi la possibilité d'exécuter ces algorithmes en parallèle sur un même processeur. Dans le même temps, les cartes graphiques sont passées d'un statut de simple périphérique d'affichage graphique à celui de supercalculateur. Elles jouissent désormais d'une attention toute particulière de la part de la communauté traitant de la simulation physique. Afin de passer au large échelle et d'être générique sur la machine d'exécution, nous avons proposé des modèles unifiés et adaptatifs de correspondance entre les algorithmes de détection de collision et les architectures machines de type multi-coeur et multi-GPU. Nous avons ainsi défini des solutions innovantes et performantes permettant de réduire significativement le temps de calcul au sein d'environnements large échelle tout en assurant la pérennité des résultats. Nos modèles couvrent l'intégralité du pipeline de détection de collision en se focalisant aussi bien sur des algorithmes de bas ou de haut niveau. Nos modèles multi-coeur, GPU et multi-GPU allient différentes techniques de subdivision spatiale à des algorithmes basés topologie ainsi que des techniques d'équilibrage de charge basées sur le vol de données. Notre solution hybride permet d'accroitre l'espace et le temps de calcul ainsi que le passage au large échelle. L'association de ces nouveaux algorithmes nous a permis de concevoir deux modèles d'adaptation algorithmique dynamique basés, ou non, sur des scénarios de pré-calcul hors-ligne. Enfin, il nous est apparu indispensable d'ajouter au pipeline de détection de collision une nouvelle dimension révélant la prise en compte des architectures pour une exécution optimale. Grâce à ce formalisme, nous avons proposé un nouveau pipeline de détection de collision offrant une granularité de parallélisme sur processeurs multi-coeur. Il permet une exécution simultanée des différentes étapes du pipeline ainsi qu'un parallélisme interne à chacune de ces étapes

Détection de Collision pour environnements large échelle : modèle unifié et adaptatif sur architectures multi-coeur et multi-GPU

Les environnements de réalité virtuelle devenant de plus en plus complexes et de très grandes dimensions, un niveau d'interaction temps-réel devient impossible à garantir. En effet, de par leur complexité, due à une géométrie détaillée et aux propriétés physiques spécifiques, ces environnements large échelle engendrent un goulet d'étranglement calculatoire critique sur les algorithmes de simulation physique. Nous avons focalisé nos travaux sur la première étape de ces algorithmes qui concerne la détection de collision car les problématiques font partie intégrante de ce goulet d'étranglement et leur complexité peut parfois se révéler quadratique dans certaines situations. Le profond bouleversement que subissent les architectures machines depuis quelques années ouvre une nouvelle voie pour réduire le goulet d'étranglement. La multiplication du nombre de cœurs offre ainsi la possibilité d'exécuter ces algorithmes en parallèle sur un même processeur. Dans le même temps, les cartes graphiques sont passées d'un statut de simple périphérique d'affichage graphique à celui de supercalculateur. Elles jouissent désormais d'une attention toute particulière de la part de la communauté traitant de la simulation physique. Afin de passer au large échelle et d'être générique sur la machine d'exécution, nous avons proposé des modèles unifiés et adaptatifs de correspondance entre les algorithmes de détection de collision et les architectures machines de type multi-cœur et multi-GPU. Nous avons ainsi défini des solutions innovantes et performantes permettant de réduire significativement le temps de calcul au sein d'environnements large échelle tout en assurant la pérennité des résultats. Nos modèles couvrent l'intégralité du pipeline de détection de collision en se focalisant aussi bien sur des algorithmes de bas ou de haut niveau. Nos modèles multi-cœur et simple GPU allient différentes techniques de subdivision spatiale à des algorithmes basés topologie ainsi que des technique d'équilibrage de charge basées sur le vol de données. Notre solution hybrides permet d'accroitre l'espace et le temps de calcul ainsi que le passage au large échelle. L'association de ces nouveaux algorithmes nous a permis de concevoir deux modèles d'adaptation algorithmique dynamique basés, ou non, sur des scénarios de pré-calcul hors-ligne. Enfin, il nous est apparu indispensable d'ajouter au pipeline de détection de collision une nouvelle dimension révélant la prise en compte des architectures pour une exécution optimale. Grâce à ce formalisme, nous avons proposé un nouveau pipeline de détection de collision offrant une granularité de parallélisme sur processeurs multi-cœur ou plateformes multi-GPU. Il permet une exécution simultanée des différentes étapes du pipeline ainsi qu'un parallélisme interne à chacune de ces étapes.Virtual reality environments are becoming increasingly large and complex, a real-time interaction level is becoming impossible to insure. Indeed, because of their complexity, due to a detailed geometry and specific physical properties, these large scale environments create a critical computational bottleneck on the physical algorithms. We focused our work on the first step of these algorithms concerning the collision detection process because the issues are an integral part of this bottleneck and their complexity can sometimes be quadratic in few situations. The deep upheaval that suffer hardware architectures those last years has opened a new way to reduce this bottleneck. Multiplying the number of cores offers the ability to execute these algorithms in parallel on one single processor. At the same time, the graphics cards have gone from being a simple graphical display device to a supercomputer. They now enjoy special attention from the community dealing with physical simulation. To perform large scale simulation and be generic on the runtime architecture, we proposed unified and adaptive mapping models between collision detection algorithms and the runtime architecture using multi-core and multi-GPU. We have developed innovative and effective solutions to significantly reduce the computation time in large scale environments while ensuring the sustainability of results. Our models cover the global collision detection pipeline, focusing both on algorithms of low or high level. Our models based on single computational unit such as multi-core and GPU combine different techniques of spatial subdivision algorithms based on topology and load balancing techniques based on data stealing. Our hybrid solution enables to increase space and computing time within large scale virtual environments. The coupling of these new algorithms enabled us to develop two models of dynamic algorithmic adaptation based (or not) on off-line precomputations scenarios. Finally, it became necessary to add to the collision detection pipeline a new dimension representing the taking account of the architecture for optimal execution. With this formalism, we proposed a new pipeline collision detection with a granularity of parallelism on multicore processors or multi-GPU platforms. It enables simultaneous execution of different stages of the pipeline and a parallel internal to each of these steps.RENNES-INSA (352382210) / SudocSudocFranceF

Synchronization-Free Parallel Collision Detection Pipeline

International audienceWe present a first parallel and adaptive collision detection pipeline running on a multi-core architecture. This pipeline integrates a first global synchronization-free parallelization of its major steps and enables to dynamically adapt the parallelism repartition during the simulation. We propose to break the sequentiality of the pipeline by simultaneously executing the two main phases (broad and narrow). We introduce and use a new buffer structure to share objects pairs between threads. To fully exploit multi-core performance, we propose a new dynamic load balancing technique to distribute threads among phases of the pipeline. This dynamic threads balancing acts on the broad and narrow phases in relation to their computation time. This technique favors the longest phase by giving it more CPU threads to run in parallel. Results show that this new generation of parallel pipeline enables to adapt computations to the simulation scenario evolution and to the run-time architecture. We tested our solution on a 8*cores architecture and performance measurements show that this first parallel pipeline is well-suited for the collision detection problem and enables to significantly reduce computation time compared to the sequential one