Extended Reality unlocks the capability to create virtual workspaces that address and exceed the limitations of existing physical multimonitor arrangements. We extend the ergonomic benefits of virtual workspaces by applying rotational assistance based on user gaze transitions between displays - meaning as a user looks towards a given display, the workspace would counter-rotate to reduce the amount of head/neck rotation required to view said display. Where prior work examined rotational assistance on one axis (horizontal) we extend this to movements across two axes, examining its impact on horizontal, vertical, and mixed arrangements of display. We found in a user study (n=20) rotational assistance improves ergonomic comfort, decreases necessary head/neck movement, improves workload, and decreases fatigue when viewing wide and tall virtual display spaces, further motivating the transition from physical to virtual displays for productivity.

The literature has demonstrated the advantages of Mixed Reality (MR) for product configuration by providing a more engaging and effective end-user experience. While collaborative and remote design tools in MR have been widely explored in previous studies, a noticeable gap remains in the exploration of co-located product configuration for couples. This gap is noteworthy since in many industries, couples (e.g., friends, partners) often make purchasing decisions together in physical retail environments. In this paper, we introduce MR-CoCo, an open MR testbed designed to explore collaborative configurations by co-located couples, both in the role of customers. The testbed is developed in Unity and features: (i) a shared MR space with virtual product 3D model anchoring, (ii) shared visualization of the current configuration, (iii) a versatile UI for selecting configuration areas, (iv) hand gestures for 3D drag and drop of colors and materials from 3D catalog to the product. A case study of the personalization of a sailboat is provided as proof of concept. The user study involved 24 couples (48 participants in total), simulating a purchasing experience and the related configuration using MR-CoCo. We assessed usability through post-experience evaluations, with the System Usability Scale (SUS) and the Co-Presence Configuration Questionnaire (CCQ) to measure collaboration and decision-making. The results demonstrated a high level of usability and perceived quality of collaboration. We also explore guidelines that can be used for remote collaboration applications, enabling configuration across a wide range of industries (e.g., automotive and clothing).

Existing state-of-the-art vertical autoscalers for containerized environments are traditionally built for cloud applications, which might behave differently than HPC workloads with their dynamic resource consumption. In these environments, autoscalers may create an inefficient resource allocation. This work analyzes nine representative HPC applications with different memory consumption patterns. Our results identify the limitations and inefficiencies of the Kubernetes Vertical Pod Autoscaler (VPA) for enabling memory elastic execution of HPC applications. We propose, implement, and evaluate ARC-V. This policy leverages both in-flight resource updates of pods in Kubernetes and the knowledge of memory consumption patterns of HPC applications for achieving elastic memory resource provisioning at the node level. Our results show that ARC-V can effectively save memory while eliminating out-of-memory errors compared to the standard Kubernetes VPA.

In this workshop submission, we reflect on the need to balance a breadth of design considerations when supporting mobile, spatial productivity. Whilst performance, ergonomics and usability remain key, there is an increasing realisation that the social impact of our designs must also be considered - from the social comfort and acceptability of a given workspace or interaction technique, to the social collisions they provoke with other passengers, to the environmental and social awareness the design facilitates in allowing the user to focus on their task whilst maintaining awareness of their environment and those around them.

Optimizing iPIC3D, an implicit Particle-in-Cell (PIC) code, for large-scale 3D plasma simulations is crucial for space and astrophysical applications. This work focuses on characterizing iPIC3D's communication efficiency through strategic measures like optimal node placement, communication and computation overlap, and load balancing. Profiling and tracing tools are employed to analyze iPIC3D's communication efficiency and provide practical recommendations. Implementing optimized communication protocols addresses the Geospace Environmental Modeling (GEM) magnetic reconnection challenges in plasma physics with more precise simulations. This approach captures the complexities of 3D plasma simulations, particularly in magnetic reconnection, advancing space and astrophysical research.

The new emerging scientific workloads to be executed in the upcoming exascale supercomputers face major challenges in terms of storage, given their extreme volume of data. In particular, intelligent data placement, instrumentation, and workflow handling are central to application performance. The IO-SEA project developed multiple solutions to aid the scientific community in adressing these challenges: a Workflow Manager, a hierarchical storage management system, and a semantic API for storage. All of these major products incorporate additional minor products that support their mission. In this paper, we discuss both the roles of all these products and how they can assist the scientific community in achieving exascale performance.

OpenCUBE aims to develop an open-source full software stack for Cloud computing blueprint deployed on EPI hardware, adaptable to emerging workloads across the computing continuum. OpenCUBE prioritizes energy awareness and utilizes open APIs, Open Source components, advanced SiPearl Rhea processors, and RISC-V accelerator. The project leverages representative workloads, such as cloud-native workloads and workflows of weather forecast data management, molecular docking, and space weather, for evaluation and validation.

High-performance GPU-accelerated particle filter methods are critical for object detection applications, ranging from autonomous driving, robot localization, to time-series prediction. In this work, we investigate the design, development and optimization of particle-filter using half-precision on CUDA cores and compare their performance and accuracy with single- and double-precision baselines on Nvidia V100, A100, A40 and T4 GPUs. To mitigate numerical instability and precision losses, we introduce algorithmic changes in the particle filters. Using half-precision leads to a performance improvement of 1.5-2x and 2.5-4.6x with respect to single- and double-precision baselines respectively, at the cost of a relatively small loss of accuracy.

Large-scale HPC simulations of plasma dynamics in fusion devices require efficient parallel I/O to avoid slowing down the simulation and to enable the post-processing of critical information. Such complex simulations lacking parallel I/O capabilities may encounter performance bottlenecks, hindering their effectiveness in data-intensive computing tasks. In this work, we focus on introducing and enhancing the efficiency of parallel I/O operations in Particle-in-Cell Monte Carlo simu-lations. We first evaluate the scalability of BIT1, a massively-parallel electrostatic PIC MC code, determining its initial write throughput capabilities and performance bottlenecks using an HPC I/O performance monitoring tool, Darshan. We design and develop an adaptor to the openPMD I/O interface that allows us to stream PIC particle and field information to I/O using the BP4 backend, aggressively optimized for I/O efficiency, including the highly efficient ADIOS2 interface. Next, we explore advanced optimization techniques such as data compression, aggregation, and Lustre file striping, achieving write throughput improvements while enhancing data storage efficiency. Finally, we analyze the enhanced high-throughput parallel I/O and storage capabilities achieved through the integration of openPMD with rapid metadata extraction in BP4 format. Our study demonstrates that the integration of openPMD and advanced I/O optimizations significantly enhances BIT1's I/O performance and storage capabilities, successfully introducing high throughput parallel I/O and surpassing the capabilities of traditional file I/O.

Extended Reality (XR) offers the potential to transform the passenger experience by allowing users to inhabit varied virtual spaces for entertainment, work or social interaction, whilst escaping the constrained transit environment. XR allows remote collaborators to feel like they are together and enables them to perform complex 3D tasks. However, the social and physical constraints of the passenger space pose unique challenges to productive and socially acceptable collaboration. Using a collaborative VR puzzle task, we examined the effects of five different f-formations of collaborator placement and orientation in an interactive workspace on social presence, task workload, and implications for social acceptability. Our quantitative and qualitative results showed that face-to-face formations were preferred for tasks with a high need for verbal communication but may lead to social collisions, such as inadvertently staring at a neighbouring passenger, or physical intrusions, such as gesturing in another passenger's personal space. More restrictive f-formations, however, were preferred for passenger use as they caused fewer intrusions on other passengers' visual and physical space.

Recent development in lightweight OS-level virtualization, containers, provides a potential solution for running HPC applications on the cloud platform. In this work, we focus on the impact of different layers in a containerized environment when migrating HPC containers from a dedicated HPC system to a cloud platform. On three ARM-based platforms, including the latest Nvidia Grace CPU, we use six representative HPC applications to characterize the impact of container virtualization, host OS and kernel, and rootless and privileged container execution. Our results indicate less than 4% container overhead in DGEMM, miniMD, and XSBench, but 8%-10% overhead in FFT, HPCG, and Hypre. We also show that changing between the container execution modes results in negligible performance differences in the six applications.

Fundamental to Augmented Reality (AR) headsets is their capacity to visually and aurally sense the world around them, necessary to drive the positional tracking that makes rendering 3D spatial content possible. This requisite sensing also opens the door for more advanced AR-driven activities, such as augmented perception, volumetric capture and biometric identification - activities with the potential to expose bystanders to significant privacy risks. Existing Privacy-Enhancing Technologies (PETs) often safeguard against these risks at a low level e.g., instituting camera access controls. However, we argue that such PETs are incompatible with the need for always-on sensing given AR headsets' intended everyday use. Through an online survey (N=102), we examine bystanders' awareness of, and concerns regarding, potentially privacy infringing AR activities; the extent to which bystanders' consent should be sought; and the level of granularity of information necessary to provide awareness of AR activities to bystanders. Our findings suggest that PETs should take into account the AR activity type, and relationship to bystanders, selectively facilitating awareness and consent. In this way, we can ensure bystanders feel their privacy is respected by everyday AR headsets, and avoid unnecessary rejection of these powerful devices by society.

The conventional model of resource allocation in HPC systems is static. Thus, a job cannot leverage newly available resources in the system or release underutilized resources during the execution. In this paper, we present Kub, a methodology that enables elastic execution of HPC workloads on Kubernetes so that the resources allocated to a job can be dynamically scaled during the execution. One main optimization of our method is to maximize the reuse of the originally allocated resources so that the disruption to the running job can be minimized. The scaling procedure is coordinated among nodes through remote procedure calls on Kubernetes for deploying workloads in the cloud. We evaluate our approach using one synthetic benchmark and two production-level MPI-based HPC applications - GRO-MACS and CM1. Our results demonstrate that the benefits of adapting the allocated resources depend on the workload characteristics. In the tested cases, a properly chosen scaling point for increasing resources during execution achieved up to 2x speedup. Also, the overhead of checkpointing and data reshuffling significantly influences the selection of optimal scaling points and requires application-specific knowledge.

Extended Reality (XR) technology brings exciting possibilities for aeroplane passengers, allowing them to escape their limited cabin space. Using nearby physical surfaces enables a connection with the real world while improving the XR experience through touch. However, available surfaces may be located in awkward positions, reducing comfort and input performance and thus limiting their long-term use. We explore the usability of passive haptic surfaces in different orientations, assessing their effects on input performance, user experience and comfort. We then overcome ergonomic issues caused by the confined space by using perceptual manipulation techniques that remap the position and rotation of physical surfaces and user movements, assessing their effects on task workload, comfort and presence. Our results show that the challenges posed by constrained seating environments can be overcome by a combination of passive haptics and remapping the workspace with moderate translation and rotation manipulations. These manipulations allow for good input performance, low workload and comfortable interaction, opening up XR use while in transit.

Complex workflows play a critical role in accelerating scientific discovery. In many scientific domains, efficient workflow management can lead to faster scientific output and broader user groups. Workflows that can leverage resources across the boundary between cloud and HPC are a strong driver for the convergence of HPC and cloud. This study investigates the transition and deployment of a GPU-accelerated molecular docking workflow that was designed for HPC systems onto a cloud-native environment with Kubernetes and Apache Airflow. The case study focuses on state-of-of-the-art molecular docking software for drug discovery. We provide a DAG-based implementation in Apache Airflow and technical details for GPU-accelerated deployment. We evaluated the workflow using the SWEETLEAD bioinformatics dataset and executed it in a Cloud environment with heterogeneous computing resources. Our workflow can effectively overlap different stages when mapped onto different computing resources.

Choral singing is a creative process that involves continuous, organized, nonverbal communication between conductors and singers. Since the COVID pandemic, choirs are moved to videoconferencing systems for rehearsals. However, the limitation of 2D video interfaces restricts the nonverbal communication, spatial awareness, and sense of presence in choral rehearsal. We designed, implemented, and evaluated VRChoir, a VR-based platform for choir rehearsals to improve these pain points. We evaluated VRChoir with conductors and singers with experience rehearsing in a remote environment. Our findings reveal that VR can be a starting point for improving the sense of presence and quality of non-verbal communication in remote music rehearsals.

Augmented Reality (AR) headsets could significantly improve the passenger experience, freeing users from the restrictions of physical smartphones, tablets and seatback displays. However, the confined space of public transport and the varying proximity to other passengers may restrict what interaction techniques are deemed socially acceptable for AR users - particularly considering current reliance on mid-air interactions in consumer headsets. We contribute and utilize a novel approach to social acceptability video surveys, employing mixed reality composited videos to present a real user performing interactions across different virtual transport environments. This approach allows for controlled evaluation of perceived social acceptability whilst freeing researchers to present interactions in any simulated context. Our resulting survey (N=131) explores the social comfort of body, device, and environment-based interactions across seven transit seating arrangements. We reflect on the advantages of discreet inputs over mid-air and the unique challenges of face-to-face seating for passenger AR.

Standalone Virtual Reality (VR) headsets can be used when travelling in cars, trains and planes. However, the constrained spaces around transport seating can leave users with little physical space in which to interact using their hands or controllers, and can increase the risk of invading other passengers' personal space or hitting nearby objects and surfaces. This hinders transport VR users from using most commercial VR applications, which are designed for unobstructed 1-2m 360° home spaces. In this paper, we investigated whether three at-a-distance interaction techniques from the literature could be adapted to support common commercial VR movement inputs and so equalise the interaction capabilities of at-home and on-transport users: Linear Gain, Gaze-Supported Remote Hand, and AlphaCursor. First, we analysed commercial VR experiences to identify the most common movement inputs so that we could create gamified tasks based on them. We then investigated how well each technique could support these inputs from a constrained $50\mathrm{x}50\text{cm}$ space (representative of an economy plane seat) through a user study $(\mathrm{N}=16)$, where participants played all three games with each technique. We measured task performance, unsafe movements (play boundary violations, total arm movement) and subjective experience and compared results to a control ‘at-home’ condition (with unconstrained movement) to determine how similar performance and experience were. Results showed that Linear Gain was the best technique, with similar performance and user experience to the ‘at-home’ condition, albeit at the expense of a high number of boundary violations and large arm movements. In contrast, AlphaCursor kept users within bounds and minimised arm movement, but suffered from poorer performance and experience. Based on the results, we provide eight guidelines for the use of, and research into, at-a-distance techniques and constrained spaces.

Remote collaborative work has become pervasive in many settings, ranging from engineering to medical professions. Users are immersed in virtual environments and communicate through life-sized avatars that enable face-to-face collaboration. Within this context, users often collaboratively view and interact with virtual 3D models, for example to assist in the design of new devices such as customized prosthetics, vehicles or buildings. Discussing such shared 3D content face-to-face, however, has a variety of challenges such as ambiguities, occlusions, and different viewpoints that all decrease mutual awareness, which in turn leads to decreased task performance and increased errors. To address this challenge, we introduce MAGIC, a novel approach for understanding pointing gestures in a face-to-face shared 3D space, improving mutual understanding and awareness. Our approach distorts the remote user's gestures to correctly reflect them in the local user's reference space when face-to-face. To measure what two users perceive in common when using pointing gestures in a shared 3D space, we introduce a novel metric called pointing agreement. Results from a user study suggest that MAGIC significantly improves pointing agreement in face-toface collaboration settings, improving co-presence and awareness of interactions performed in the shared space. We believe that MAGIC improves remote collaboration by enabling simpler communication mechanisms and better mutual awareness.