High-performance computing (HPC) systems generate massive scientific datasets, often stored in remote data repositories. Limited bandwidth and resource-constrained end-points pose challenges for efficient large data transfer. Error-bounded lossy compression addresses this by reducing data sizes (higher bit-rates) while controlling distortion. However, different compressors exhibit distinct rate-distortion behaviors even under the same error bounds. Thus, selecting an optimal compressor before transfer is essential to meet ndpoint-specific requirements e.g., maximizing data reduction at a fixed distortion. Existing trial-and-error approaches require multiple costly full-scale compression runs to reach at target requirements, making them impractical for such online use. To address this, we propose OPTRD, a compressor-agnostic framework that efficiently models rate-distortion trade-offs across multiple lossy compressors by analyzing spatial data traits at reduced resolutions. Evaluated using 3 state-of-the-art lossy compressors on 30 scientific datasets from 4 HPC applications, OPTRD incurs only similar to 5% average estimation error and achieves over 100x runtime speedup compared to trial-and-error methods, significantly improving optimal compressor selection during such data transfer use cases.
Public availability of data about the supply and utilization of Dockless Electric Scooters (DESs) is needed to develop smart mobility applications to promote the use of sustainable, shared, and equitable transportation. Prior studies have analyzed some attributes of DES usage, but they are limited in three aspects. 1) Data collection methods: Prior studies relied on DES providers to provide usage data to authors. Unfortunately, convincing DES providers to provide complete usage data is extremely hard, if not impossible. Thus, this approach is not scalable and further limits the ability to study DES usage trends only in the regions and only at the times for which the DES providers provide the data. 2) Global DES usage analysis: Prior studies have not analyzed how DES usage varies in different parts of the world. 3) Aspects of the usage analysis: Prior studies have not analyzed important DES usage attributes such as the availability of DES in minority-concentrated areas, temporal trends of DES trips from different types of locations, etc. This paper fills the gap in prior work by addressing these three limitations. For data collection methods, we present novel techniques to continuously collect DES usage data in any city worldwide without relying on DES providers. For global DES usage analysis, we study the spatiotemporal DES usage trends of 12 DES providers in 11 cities worldwide. For the aspects of the usage analysis, we provide extensive analysis of various aspects of DES usage that have previously either never been studied or studied only briefly. These aspects include DES supply trends, competition among providers, supply and trip hotspots, equitable DES availability, and so on. The methods and findings that we present in this paper aid in understanding DES usage across the world and offer actionable insights to DES stakeholders.
GPU-accelerated molecular dynamics (MD) simulations have enabled significant advances in modeling biomolecular systems and extended timescales. While system size, typically measured by atom count, is often used to estimate simulation performance, the impact of biophysical system composition remains underexplored. This study investigated GPU-accelerated simulation throughput using GROMACS on an NVIDIA Titan RTX GPU across eight atom count-controlled systems (approximately 82,000 atoms) with varying biophysical compositions. The benchmark set included water-only, protein-ligand, membrane-only, and membrane-protein systems, matched to the standard benchMEM system for controlled comparison. MD systems with high water and protein content consistently demonstrated superior GPU performance exceeding 30% compared to lipid-rich systems, despite equivalent atom counts. Membrane-protein systems exhibited modest improvements over membrane-only systems, likely due to more favorable domain decomposition properties. These findings provide quantitative insight into the influence of system composition on GPU performance and offer practical information for optimizing MD simulation systems in terms of biological complexity and computational efficiency.
This paper investigates a multi-user downlink communication system where an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) assists a base station employing rate-splitting multiple access (RSMA) under hardware impairments. A sum-rate maximization problem is formulated, jointly optimizing the common rate allocation, beamforming vectors, and IRS phase shifts. To tackle the non-convexity, an alternating optimization framework is developed in which each subproblem is efficiently solved using successive convex approximation (SCA). Simulation results demonstrate that the proposed design outperforms conventional benchmarks.
Deploying AI models on low-power hardware remains limited by severe constraints in memory, compute throughput, and energy efficiency. This project explores whether the ESP32-S3, a low-cost microcontroller with SIMD acceleration, can run a lightweight AI model efficiently.
