SPH-EXA: Enhancing the Scalability of SPH codes Via an Exascale-Ready SPH Mini-App

Numerical simulations of fluids in astrophysics and computational fluid dynamics (CFD) are among the most computationally-demanding calculations, in terms of sustained floating-point operations per second, or FLOP/s. It is expected that these numerical simulations will significantly benefit from the future Exascale computing infrastructures, that will perform 10^18 FLOP/s. The performance of the SPH codes is, in general, adversely impacted by several factors, such as multiple time-stepping, long-range interactions, and/or boundary conditions. In this work an extensive study of three SPH implementations SPHYNX, ChaNGa, and XXX is performed, to gain insights and to expose any limitations and characteristics of the codes. These codes are the starting point of an interdisciplinary co-design project, SPH-EXA, for the development of an Exascale-ready SPH mini-app. We implemented a rotating square patch as a joint test simulation for the three SPH codes and analyzed their performance on a modern HPC system, Piz Daint. The performance profiling and scalability analysis conducted on the three parent codes allowed to expose their performance issues, such as load imbalance, both in MPI and OpenMP. Two-level load balancing has been successfully applied to SPHYNX to overcome its load imbalance. The performance analysis shapes and drives the design of the SPH-EXA mini-app towards the use of efficient parallelization methods, fault-tolerance mechanisms, and load balancing approaches.Comment: arXiv admin note: substantial text overlap with arXiv:1809.0801

Sparse Volumetric Deformation

Volume rendering is becoming increasingly popular as applications require realistic solid shape representations with seamless texture mapping and accurate filtering. However rendering sparse volumetric data is difficult because of the limited memory and processing capabilities of current hardware. To address these limitations, the volumetric information can be stored at progressive resolutions in the hierarchical branches of a tree structure, and sampled according to the region of interest. This means that only a partial region of the full dataset is processed, and therefore massive volumetric scenes can be rendered efficiently. The problem with this approach is that it currently only supports static scenes. This is because it is difficult to accurately deform massive amounts of volume elements and reconstruct the scene hierarchy in real-time. Another problem is that deformation operations distort the shape where more than one volume element tries to occupy the same location, and similarly gaps occur where deformation stretches the elements further than one discrete location. It is also challenging to efficiently support sophisticated deformations at hierarchical resolutions, such as character skinning or physically based animation. These types of deformation are expensive and require a control structure (for example a cage or skeleton) that maps to a set of features to accelerate the deformation process. The problems with this technique are that the varying volume hierarchy reflects different feature sizes, and manipulating the features at the original resolution is too expensive; therefore the control structure must also hierarchically capture features according to the varying volumetric resolution. This thesis investigates the area of deforming and rendering massive amounts of dynamic volumetric content. The proposed approach efficiently deforms hierarchical volume elements without introducing artifacts and supports both ray casting and rasterization renderers. This enables light transport to be modeled both accurately and efficiently with applications in the fields of real-time rendering and computer animation. Sophisticated volumetric deformation, including character animation, is also supported in real-time. This is achieved by automatically generating a control skeleton which is mapped to the varying feature resolution of the volume hierarchy. The output deformations are demonstrated in massive dynamic volumetric scenes

A Geometric Approach for Deciphering Protein Structure from Cryo-EM Volumes

Electron Cryo-Microscopy or cryo-EM is an area that has received much attention in the recent past. Compared to the traditional methods of X-Ray Crystallography and NMR Spectroscopy, cryo-EM can be used to image much larger complexes, in many different conformations, and under a wide range of biochemical conditions. This is because it does not require the complex to be crystallisable. However, cryo-EM reconstructions are limited to intermediate resolutions, with the state-of-the-art being 3.6A, where secondary structure elements can be visually identified but not individual amino acid residues. This lack of atomic level resolution creates new computational challenges for protein structure identification. In this dissertation, we present a suite of geometric algorithms to address several aspects of protein modeling using cryo-EM density maps. Specifically, we develop novel methods to capture the shape of density volumes as geometric skeletons. We then use these skeletons to find secondary structure elements: SSEs) of a given protein, to identify the correspondence between these SSEs and those predicted from the primary sequence, and to register high-resolution protein structures onto the density volume. In addition, we designed and developed Gorgon, an interactive molecular modeling system, that integrates the above methods with other interactive routines to generate reliable and accurate protein backbone models

Estimating Anthropometric Marker Locations from 3-D LADAR Point Clouds

An area of interest for improving the identification portion of the system is in extracting anthropometric markers from a Laser Detection and Ranging (LADAR) point cloud. Analyzing anthropometrics markers is a common means of studying how a human moves and has been shown to provide good results in determining certain demographic information about the subject. This research examines a marker extraction method utilizing principal component analysis (PCA), self-organizing maps (SOM), alpha hulls, and basic anthropometric knowledge. The performance of the extraction algorithm is tested by performing gender classification with the calculated markers

3D segmentation and localization using visual cues in uncontrolled environments

3D scene understanding is an important area in robotics, autonomous vehicles, and virtual reality. The goal of scene understanding is to recognize and localize all the objects around the agent. This is done through semantic segmentation and depth estimation. Current approaches focus on improving the robustness to solve each task but fail in making them efficient for real-time usage. This thesis presents four efficient methods for scene understanding that work in real environments. The methods also aim to provide a solution for 2D and 3D data. The first approach presents a pipeline that combines the block matching algorithm for disparity estimation, an encoder-decoder neural network for semantic segmentation, and a refinement step that uses both outputs to complete the regions that were not labelled or did not have any disparity assigned to them. This method provides accurate results in 3D reconstruction and morphology estimation of complex structures like rose bushes. Due to the lack of datasets of rose bushes and their segmentation, we also made three large datasets. Two of them have real roses that were manually labelled, and the third one was created using a scene modeler and 3D rendering software. The last dataset aims to capture diversity, realism and obtain different types of labelling. The second contribution provides a strategy for real-time rose pruning using visual servoing of a robotic arm and our previous approach. Current methods obtain the structure of the plant and plan the cutting trajectory using only a global planner and assume a constant background. Our method works in real environments and uses visual feedback to refine the location of the cutting targets and modify the planned trajectory. The proposed visual servoing allows the robot to reach the cutting points 94% of the time. This is an improvement compared to only using a global planner without visual feedback, which reaches the targets 50% of the time. To the best of our knowledge, this is the first robot able to prune a complete rose bush in a natural environment. Recent deep learning image segmentation and disparity estimation networks provide accurate results. However, most of these methods are computationally expensive, which makes them impractical for real-time tasks. Our third contribution uses multi-task learning to learn the image segmentation and disparity estimation together end-to-end. The experiments show that our network has at most 1/3 of the parameters of the state-of-the-art of each individual task and still provides competitive results. The last contribution explores the area of scene understanding using 3D data. Recent approaches use point-based networks to do point cloud segmentation and find local relations between points using only the latent features provided by the network, omitting the geometric information from the point clouds. Our approach aggregates the geometric information into the network. Given that the geometric and latent features are different, our network also uses a two-headed attention mechanism to do local aggregation at the latent and geometric level. This additional information helps the network to obtain a more accurate semantic segmentation, in real point cloud data, using fewer parameters than current methods. Overall, the method obtains the state-of-the-art segmentation in the real datasets S3DIS with 69.2% and competitive results in the ModelNet40 and ShapeNetPart datasets

An investigation into adaptive power reduction techniques for neural hardware

In light of the growing applicability of Artificial Neural Network (ANN) in the signal processing field [1] and the present thrust of the semiconductor industry towards lowpower SOCs for mobile devices [2], the power consumption of ANN hardware has become a very important implementation issue. Adaptability is a powerful and useful feature of neural networks. All current approaches for low-power ANN hardware techniques are ‘non-adaptive’ with respect to the power consumption of the network (i.e. power-reduction is not an objective of the adaptation/learning process). In the research work presented in this thesis, investigations on possible adaptive power reduction techniques have been carried out, which attempt to exploit the adaptability of neural networks in order to reduce the power consumption. Three separate approaches for such adaptive power reduction are proposed: adaptation of size, adaptation of network weights and adaptation of calculation precision. Initial case studies exhibit promising results with significantpower reduction