The increasing computing requirements for GPUs (Graphics Processing Units) have favoured the design and marketing of commodity devices that nowadays can also be used to accelerate general purpose computing. Therefore, future high performance clusters intended for HPC (High Performance Computing) will likely include such devices. However, high-end GPU-based accelerators used in HPC feature a considerable energy consumption, so that attaching a GPU to every node of a cluster has a strong impact on its overall power consumption. In this paper we detail a framework that enables remote GPU acceleration in HPC clusters, thus allowing a reduction in the number of accelerators installed in the cluster. This leads to energy, acquisition, maintenance, and space savings.

The high-performance computing domain is enriching with the inclusion of Networks-on-chip (NoCs) as a key component of many-core (CMPs or MPSoCs) architectures. NoCs face the communication scalability challenge while meeting tight power, area and latency constraints. Designers must address new challenges that were not present before. Defective components, the enhancement of application-level parallelism or power-aware techniques may break topology regularity, thus, efficient routing becomes a challenge.In this paper, uLBDR (Universal Logic-Based Distributed Routing) is proposed as an efficient logic-based mechanism that adapts to any irregular topology derived from 2D meshes, being an alternative to the use of routing tables (either at routers or at end-nodes). uLBDR requires a small set of configuration bits, thus being more practical than large routing tables implemented in memories. Several implementations of uLBDR are presented highlighting the trade-off between routing cost and coverage. The alternatives span from the previously proposed LBDR approach (with 30\% of coverage) to the uLBDR mechanism achieving full coverage. This comes with a small performance cost, thus exhibiting the trade-off between fault tolerance and performance.

The hardware and software advances of Graphics Processing Units (GPUs) have favored the development of GPGPU (General-Purpose Computation on GPUs) and its adoption in many scientific, engineering, and industrial areas. Thus, GPUs are increasingly being introduced in high-performance computing systems as well as in datacenters. On the other hand, virtualization technologies are also receiving rising interest in these domains, because of their many benefits on acquisition and maintenance savings. There are currently several works on GPU virtualization. However, there is no standard solution allowing access to GPGPU capabilities from virtual machine environments like, e.g., VMware, Xen, VirtualBox, or KVM. Such lack of a standard solution is delaying the integration of GPGPU into these domains. In this paper, we propose a first step towards a general and open source approach for using GPGPU features within VMs. In particular, we describe the use of rCUDA, a GPGPU (General-Purpose Computation on GPUs) virtualization framework, to permit the execution of GPU-accelerated applications within virtual machines (VMs), thus enabling GPGPU capabilities on any virtualized environment. Our experiments with rCUDA in the context of KVM and VirtualBox on a system equipped with two NVIDIA GeForce 9800 GX2 cards illustrate the overhead introduced by the rCUDA middleware and prove the feasibility and scalability of this general virtualizing solution. Experimental results show that the overhead is proportional to the dataset size, while the scalability is similar to that of the native environment.

Climate change is one of the greatest challenges for modern societies. Its consequences, often associated with extreme events, have dramatic results worldwide. New synergies between different disciplines, including artificial intelligence (AI), Internet of Things (IoT), and edge computing can lead to radically new approaches for the real-time tracking of natural disasters that are also designed to reduce the environmental footprint. In this article, we propose an AI-based pipeline for processing natural disaster images taken from drones. The purpose of this pipeline is to reduce the number of images to be processed by the first responders of the natural disaster. It consists of three main stages: 1) a lightweight autoencoder based on deep learning; 2) a dimensionality reduction using the $t$ -distributed stochastic neighbor embedding algorithm; and 3) a fuzzy clustering procedure. This pipeline is evaluated on several edge computing platforms with low-power accelerators to assess the design of intelligent autonomous drones to provide this service in real time. Our experimental evaluation focuses on flooding, showing that the amount of information to be processed is substantially reduced, whereas edge computing platforms with low-power graphics accelerators are placed as a compelling alternative for processing these heavy computational workloads, obtaining a performance loss of only $2.3\times $ compared to its cloud counterpart version, running both the training and inference steps.

The high-performance computing domain is enriching with the inclusion of networks-on-chip (NoCs) as a key component of many-core (CMPs or MPSoCs) architectures. NoCs face the communication scalability challenge while meeting tight power, area, and latency constraints. Designers must address new challenges that were not present before. Defective components, the enhancement of application-level parallelism, or power-aware techniques may break topology regularity, thus, efficient routing becomes a challenge. This paper presents universal logic-based distributed routing (uLBDR), an efficient logic-based mechanism that adapts to any irregular topology derived from 2-D meshes, instead of using routing tables. uLBDR requires a small set of configuration bits, thus being more practical than large routing tables implemented in memories. Several implementations of uLBDR are presented highlighting the tradeoff between routing cost and coverage. The alternatives span from the previously proposed LBDR approach (with 30% of coverage) to the uLBDR mechanism achieving full coverage. This comes with a small performance cost, thus exhibiting the tradeoff between fault tolerance and performance. Power consumption, area, and delay estimates are also provided highlighting the efficiency of the mechanism. To do this, different router models (one for CMPs and one for MPSoCs) have been designed as a proof concept.