Hybrid Parallelism for Volume Rendering on Large, Multi- and Many-core Systems

With the computing industry trending towards multi- and many-core processors, we study how a standard visualization algorithm, ray-casting volume rendering, can benefit from a hybrid parallelism approach. Hybrid parallelism provides the best of both worlds: using distributed-memory parallelism across a large numbers of nodes increases available FLOPs and memory, while exploiting shared-memory parallelism among the cores within each node ensures that each node performs its portion of the larger calculation as efficiently as possible. We demonstrate results from weak and strong scaling studies, at levels of concurrency ranging up to 216,000, and with datasets as large as 12.2 trillion cells. The greatest benefit from hybrid parallelism lies in the communication portion of the algorithm, the dominant cost at higher levels of concurrency. We show that reducing the number of participants with a hybrid approach significantly improves performance

Performance evaluation of multi-core multi-cluster architecture

A multi-core cluster is a cluster composed of numbers of nodes where each node has a number of processors, each with more than one core within each single chip. Cluster nodes are connected via an interconnection network. Multi-cored processors are able to achieve higher performance without driving up power consumption and heat, which is the main concern in a single-core processor. A general problem in the network arises from the fact that multiple messages can be in transit at the same time on the same network links. This paper considers the communication latencies of a multi-core multi-cluster architecture will be investigated using simulation experiments and measurements under various working conditions

Mobile graphics: SIGGRAPH Asia 2017 course

Peer ReviewedPostprint (published version

Exploring manycore architectures for next-generation HPC systems through the MANGO approach

[EN] The Horizon 2020 MANGO project aims at exploring deeply heterogeneous accelerators for use in High-Performance Computing systems running multiple applications with different Quality of Service (QoS) levels. The main goal of the project is to exploit customization to adapt computing resources to reach the desired QoS. For this purpose, it explores different but interrelated mechanisms across the architecture and system software. In particular, in this paper we focus on the runtime resource management, the thermal management, and support provided for parallel programming, as well as introducing three applications on which the project foreground will be validated.This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 671668.Flich Cardo, J.; Agosta, G.; Ampletzer, P.; Atienza-Alonso, D.; Brandolese, C.; Cappe, E.; Cilardo, A.... (2018). Exploring manycore architectures for next-generation HPC systems through the MANGO approach. Microprocessors and Microsystems. 61:154-170. https://doi.org/10.1016/j.micpro.2018.05.011S1541706

Object-level dynamic SLAM

Visual Simultaneous Localisation and Mapping (SLAM) can estimate a camera's pose in an unknown environment and reconstruct an online map of it. Despite the advances in many real-time dense SLAM systems, most still assume a static environment, which is not a valid assumption in many real-world scenarios. This thesis aims to enable dense visual SLAM to run robustly in a dynamic environment, knowing where the sensor is in the environment, and, also importantly, what and where objects are in the surrounding environment for better scene understanding. The contributions in this thesis are threefold. The first one presents one of the first object-level dynamic SLAM systems that robustly track camera pose while detecting, tracking, and reconstructing all the objects in dynamic scenes. It can continuously fuse geometric, semantic, and motion information for each object into an octree-based volumetric representation. One of the challenges in tracking moving objects is that the object motion can easily break the illumination constancy assumption. In our second contribution, we address this issue by proposing a dense feature-metric alignment to robustly estimate camera and object poses. We will show how to learn dense feature maps and feature-metric uncertainties in a self-supervised way. They formulate a probabilistic feature-metric residual, which can be efficiently solved using Gauss-Newton optimisation and easily coupled with other residuals. So far, we can only reconstruct objects' geometry from the sensor data. Our third contribution further incorporates category-level shape prior to the object mapping. Conditioning on the depth measurement, the learned implicit function completes the unseen part while reconstructing the observed part accurately. It can yield better reconstruction completeness and more accurate object pose estimation. These three contributions in this thesis have advanced the state of the art in visual SLAM. We hope such object-level dynamic SLAM systems will help robots intelligently interact with the human-existing world.Open Acces

Coordinated Landing and Mapping with Aerial and Ground Vehicle Teams

Micro Umanned Aerial Vehicle~(UAV) and Umanned Ground Vehicle~(UGV) teams present tremendous opportunities in expanding the range of operations for these vehicles. An effective coordination of these vehicles can take advantage of the strengths of both, while mediate each other's weaknesses. In particular, a micro UAV typically has limited flight time due to its weak payload capacity. To take advantage of the mobility and sensor coverage of a micro UAV in long range, long duration surveillance mission, a UGV can act as a mobile station for recharging or battery swap, and the ability to perform autonomous docking is a prerequisite for such operations. This work presents an approach to coordinate an autonomous docking between a quadrotor UAV and a skid-steered UGV. A joint controller is designed to eliminate the relative position error between the vehicles. The controller is validated in simulations and successful landing is achieved in indoor environment, as well as outdoor settings with standard sensors and real disturbances. Another goal for this work is to improve the autonomy of UAV-UGV teams in positioning denied environments, a very common scenarios for many robotics applications. In such environments, Simultaneous Mapping and Localization~(SLAM) capability is the foundation for all autonomous operations. A successful SLAM algorithm generates maps for path planning and object recognition, while providing localization information for position tracking. This work proposes an SLAM algorithm that is capable of generating high fidelity surface model of the surrounding, while accurately estimating the camera pose in real-time. This algorithm improves on a clear deficiency of its predecessor in its ability to perform dense reconstruction without strict volume limitation, enabling practical deployment of this algorithm on robotic systems