Low-Latency Rendering With Dataflow Architectures

Recent years have seen a resurgence of virtual reality (VR), sparked by the repurposing of low-cost COTS components. VR aims to generate stimuli that appear to come from a source other than the interface through which they are delivered. The synthetic stimuli replace real-world stimuli, and transport the user to another, perhaps imaginary, “place.” To do this, we must overcome many challenges, often related to matching the synthetic stimuli to the expectations and behavior of the real world. One way in which the stimuli can fail is its latency–– the time between a user's action and the computer's response. We constructed a novel VR renderer, that optimized latency above all else. Our prototype allowed us to explore how latency affects human–computer interaction. We had to completely reconsider the interaction between time, space, and synchronization on displays and in the traditional graphics pipeline. Using a specialized architecture––dataflow computing––we combined consumer, industrial, and prototype components to create an integrated 1:1 room-scale VR system with a latency of under 3 ms. While this was prototype hardware, the considerations in achieving this performance inform the design of future VR pipelines, and our human factors studies have provided new and sometimes surprising contributions to the body of knowledge on latency in HCI

LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

Modelling of Information Flow and Resource Utilization in the EDGE Distributed Web System

The adoption of Distributed Web Systems (DWS) into modern engineering design process has dramatically increased in recent years. The Engineering Design Guide and Environment (EDGE) is one such DWS, intended to provide an integrated set of tools for use in the development of new products and services. Previous attempts to improve the efficiency and scalability of DWS focused largely on hardware utilization (i.e. multithreading and virtualization) and software scalability (i.e. load balancing and cloud services). However, these techniques are often limited to analysis of the computational complexity of the algorithms implemented. This work seeks to improve the understanding of efficiency and scalability of DWS by modelling the dynamics of information flow and resource utilization by characterizing DWS workloads through historical usage data (i.e. request type, frequency, access time). The design and implementation of EDGE is described. A DWS model of an EDGE system is developed and validated against theoretical limiting cases. The DWS model is used to predict the throughput of an EDGE system given a resource allocation and workflow. Results of the simulation suggest that proposed DWS designs can be evaluated according to the usage requirements of an engineering firm, ultimately guiding an informed decision for the selection and deployment of a DWS in an enterprise environment. Recommendations for future work related to the continued development of EDGE, DWS modelling of EDGE installation environments, and the extension of DWS modelling to new product development processes are presented