Physical Sciences and Mathematics Commons^™

Revisiting Ulam Stability For Boundary Value Problems, Martin Bohner, Snezhana Hristova, Agnieszka B. Malinowska, Ewa Girejko

Mathematics and Statistics Faculty Research & Creative Works

The main goal of this paper is to apply Ulam stability theory to boundary value problems for dynamic equations, while addressing several common misconceptions found in the existing literature. We identify the key issues that arise when applying Ulam stability to such problems and propose three distinct approaches to overcome them. To enhance clarity and accessibility, we begin with nonlinear ordinary differential equations and subsequently extend the analysis to nonlinear dynamic equations on time scales. Since a time scale is defined as any nonempty closed subset of the real numbers, our results are applicable to dynamic equations on continuous, discrete, …

Reduced Mediators Released By Cyanobacteria During Exoelectrogenesis Detected Using Differential Pulse Voltammetry, Laura T. Wey, Monica Brachi, Yagut Allahverdiyeva, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Cyanobacteria generate electrical current through exoelectrogenesis (extracellular electron transfer) downstream of photosynthesis, yet the identity of the endogenous redox mediator(s) responsible remains unresolved. Here, we apply differential pulse voltammetry (DPV) to detect and characterise redox-active species released by Synechocystis sp. PCC 6803 under illumination. To enable sensitive detection, we developed an electrolyte intermediate between BG11 (Blue-Green-11) growth medium and MOPS (3-(N-morpholino)propanesulfonic acid) buffer that minimises background electrochemical interference while maintaining short-term cellular functionality. DPV revealed multiple light-enhanced oxidation peaks (0.1–0.65 V vs. saturated calomel electrode (SCE)) that were not resolvable using cyclic voltammetry, providing evidence that cyanobacteria release reduced compounds …

Long Range Battery-Free Wireless Power Transfer Testbed For Underground Mines Iot And Lpwan Devices, Anabi Hilary Kelechi, Samuel Frimpong, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

Underground mines are susceptible to occasional roof falls and cave-ins, temporarily destroying the existing wireless communications and telemetry infrastructure. During this temporary outage, intermittent provision of electrical energy wirelessly to the already deployed low-power wireless area networks (LPWAN) and Internet of Things (IoT) devices assumes a fundamental requirement. In this article, we propose and design a long-range far-field radio frequency (RF) wireless power transfer (WPT) testbed to power LPWAN and IoT devices at 35 m in an underground mines facility. Class AB external power amplifier (PA) was introduced to achieve a long-distance RF WPT, in the 880 MHz band. Thus, …

A Review Of Gallium And Germanium Recovery From Industrial Solid Residues, Ernest V. Oteng, Marthias Silwamba, Lana Alagha, Alex Luyima

Mining Engineering Faculty Research & Creative Works

The global demand for critical elements such as gallium (Ga) and germanium (Ge), and the shift toward circular mining has accelerated the development of new extraction and recovery processing these metals from industrial solid residues. This transition is driven by a substantial surge in demand for advanced technologies and the depletion of primary ore. Consequently, industrial solid residues, particularly zinc processing residues, smelter slags, flue dusts, and coal fly ash, are emerging as viable alternative sources. These residues incorporate Ga and Ge through isomorphic substitution in phases such as ferrites, silicates, and glassy aluminosilicates, and are enriched by industrial processes, …

Online Multidimensional Multiple Choice Knapsack Path Planner For Uavs, Manisha Wadhwa, Neelima Gupta, Sanjay Madria

Computer Science Faculty Research & Creative Works

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) in safety–critical and time-sensitive applications such as disaster management, battlefield reconnaissance, urban surveillance and infrastructure inspection demands online path planning strategies. A fundamental requirement in such missions is generating flight paths that pass through some pre-specified waypoints while servicing dynamically requested spatio-temporal task points arriving randomly such as capturing aerial imagery. Existing UAV path planning approaches, including A* and its variants, meta-heuristic algorithms, and dynamic programming, find the shortest distance path from source to destination, but they are predominantly offline and assume complete prior environmental knowledge, thus, incur significantly higher computational cost …

Microwave Spectrum And Iodine Nuclear Quadrupole Coupling Constants Of Difluorodiiodomethane, Cf2i2, Michael J. Carrillo, Michael J. Tubergen, Garry S. Grubbs, S. A. Cooke, Stewart E. Novick

Chemistry Faculty Research & Creative Works

The high-resolution rotational spectrum of difluoro diiodomethane is reported for the first time. A molecular-beam broadband Fourier transform microwave spectrometer recorded the spectrum over the frequency range of 5.9–18.1 GHz. The rotational constants, centrifugal distortion constants, nuclear quadrupole coupling constants, and nuclear spin-rotation constants for the two equivalent iodine nuclei were determined from fitting 1378 hyperfine transitions of 163 rotational transitions: A = 2743.79691(18)MHz, B = 587.83103(5) MHz, C = 528.72470(5) MHz, χaa = −1213.287(6) MHz, χbb – χcc = −885.210(5) MHz, and χab = 1437.721(5) MHz. 31 dipole-forbidden/quadrupole-allowed transitions, arising from the large nuclear quadrupole coupling effect of iodine, …

Ph-Compensated Hydrogen Peroxide Quantification Using A Dual-Modal Fiber-Optic Probe, Homayoon Soleimani Dinani, Bohong Zhang, Maryam Karimi, Rex E. Gerald, Shelley D. Minteer, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

We report a dual-modal fiber-optic probe that integrates electrochemical quantification of hydrogen peroxide (H₂O₂) with co-localized fluorescent pH sensing for pH-indexed interpretation of the H₂O₂ response. H₂O₂ is a reactive oxygen species involved in oxidative stress, inflammation, and cellular signaling, and local pH modulates both its production and electrochemical response. Many electrochemical H₂O₂ sensors exhibit pH-dependent sensitivity, creating ambiguity unless pH is measured and used for compensation, which is difficult in small, heterogeneous, or rapidly changing microenvironments. A three-electrode configuration—working (WE), counter (CE), and Ag/AgCl pseudo-reference (pRE) electrodes—is fabricated directly on the cylindrical surface of a 710-µm-diameter optical fiber using …

Nontarget Analysis Of The Volatile And Semivolatile Chemical Profile Of Wildland Firefighter Gear: Insight Into Occupational Exposure And Health Implications, Patrica Ivy Agorsor, Husam Kafeenah, Margaret D. Taiwo, Robin M. Verble, Michael O. Eze

Biological Sciences Faculty Research & Creative Works

Prescribed fire releases a complex mixture of volatile and semivolatile organic compounds to the environment. Firefighters are directly exposed to these hazardous mixtures through occupational activities. This study investigated the volatile and semivolatile chemical contamination profile of firefighter protective gear and the effectiveness of laundering as a decontamination strategy. Our results revealed that firefighter gear accumulated a broad spectrum of combustion-derived contaminants, predominantly composed of polycyclic aromatic hydrocarbons and derivatives, phenolic compounds, and aromatic hydrocarbons. Representative compounds identified included toluene, biphenyl, naphthalene, 1-methynaphthalene, 2-methylnaphthalene, acenaphthene, phenol, alkylated phenols, and chlorinated phenols. Quantitative analysis revealed representative combustion-derived compounds at concentrations (mean …

A Unified Sensitivity Kernel For Coherent Wave Tomography In Radar, Seismic, Ultrasound And Optical Imaging, Alexey Yamilov

Research Data

Overview:

Six figures of the manuscript that report numerical simulations, main-text Fig. 5 and Supplementary Figs. S3 to S7, each as a PDF together with the plotted data in one CSV file per figure. The simulations are described in the Methods and in Supplementary Note S10 of the manuscript (arXiv:2609.31583). They were computed with MESTI in two dimensions and with the modified Born series in three, on the Mill and Hellbender clusters. The README describes the files and the layout of the CSV files.

Simultaneous Analysis Of Phytohormones And Lipid Peroxidation Products In Plant Seeds By Lc-Ms/Ms After Molecular Weight Cutoff Filtration, Sargun Kaur, Honglan Shi, Obed Patty, Paul Ki-Souk Nam

Chemistry Faculty Research & Creative Works

Investigation of the phytohormone and lipid peroxidation product levels in plant seeds can provide an insight into their germination potential and vigor. Simultaneous analysis of these diverse classes of compounds presents a significant challenge for a sample preparation method tailored to accommodate their distinct chemical properties. In this study, an analytical method consisting of simple extraction, molecular weight cutoff filtration, and subsequent liquid-chromatography tandem mass spectrometry (LC-MS/MS) analysis was developed for accurate quantification of 11 phytohormones including 2 auxins, 4 cytokinins, 2 gibberellins, abscisic acid, salicylic acid, and jasmonic acid in plant seeds. Additionally, the new method was used for …

Toxicity Ahead: Forecasting Conversational Derailment On Github, Mia Mohammad Imran, Robert Zita, Rahat Rizvi Rahman, Preetha Chatterjee, Kostadin Damevski

Computer Science Faculty Research & Creative Works

Toxic interactions in Open Source Software (OSS) communities reduce contributor engagement and threaten project sustainability. Preventing such toxicity before it emerges requires a clear understanding of how harmful conversations unfold. However, most proactive moderation strategies are manual, requiring significant time and effort from community maintainers. To support more scalable approaches, we curate a dataset of 159 derailed toxic threads and 207 non-toxic threads from GitHub discussions. Our analysis reveals that toxicity can be forecast by tension triggers, sentiment shifts, and specific conversational patterns.We present a novel Large Language Model (LLM)-based framework for predicting conversational derailment on GitHub using a two-step …

On-Shell Compression And Reconstruction (Oscar) Of Monochromatic Wave Fields In Weakly Scattering Media, Pablo Jara, Alexey Yamilov

Research Data

Overview

This deposit contains one realization of each of the two simulation platforms of Ref. [1], a two-dimensional (2D) scalar field and a three-dimensional (3D) vector-field component, together with two self-contained Python scripts that perform On-Shell Compression and Reconstruction (OSCAR) on them. Each script Fourier-transforms the stored field, measures the shell radius from the spectrum, retains only the Fourier components inside the shell of full width α/ℓs around it for α = 1, 3, 5, 8, 12, reconstructs the field by the inverse transform, and plots the reconstructed intensities, panels (a)–(e), next to the original field, panel (f), on a …

Learning-Based Entanglement Generation For Quantum Routing, Tasdiqul Islam, Rasman Mubtasim Swargo, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Entanglement generation in long-distance quantum networks is challenging because resources are limited and entanglement swapping is probabilistic. To maximize the rate of successful requests, existing quantum routing algorithms often rely on computationally expensive methods such as Integer Linear Programming (ILP) to determine which links to entangle and use for end-To-end entanglement generation. However, these approaches fail to meet the latency requirements of real-world quantum networks. In this study, we propose a Reinforcement Learning (RL)-based model that determines which links to entangle in each time slot, replacing the slow ILP-based link-selection phase used in prior algorithms. The proposed Deep Q-learning model …

Individualized Bayesian Inference Identifies Novel Genetic Variants For Parkinson's Disease, Jin Ren, Yasaman J. Soofi, Md Asad Rahman, Qing Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Parkinson's disease (PD) is a complex neurodegenerative disorder with a significant genetic component. While genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with PD, the reliance on large sample sizes and population-level analyses may overlook variants with lower minor allele frequencies or individual-specific relevance. Individualized Bayesian Inference (IBI) offers a promising method to complement GWAS by identifying and prioritizing candidate genetic markers at both the individual and patients-like-me subgroup levels. This study evaluates the application of IBI to PD genetics, using GWAS as a baseline for comparison. We analyzed genetic data from the Fox Insight online …

Editorial For The Special Issue “Advances And Applications Of Polymer Gels For Subsurface Energy And Storage”, Baojun Bai, Jingyang Pu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Polymer gels are essential functional materials for subsurface energy operations, playing a critical role in conformance control, fluid diversion, hydraulic fracturing, and leakage mitigation. As reservoirs become increasingly complex and the demand for sustainable energy grows, continued innovation in gel technologies is crucial. This editorial introduces a Special Issue titled "Advances and Applications of Polymer Gels for Subsurface Energy and Storage," which compiles seven original research articles exploring recent developments in gel synthesis, characterization, and applications. The featured studies highlight the versatility of polymer gels, including nanoparticle-reinforced composites, foam–gel hybrids, recrosslinkable preformed particle gels, and advanced fracturing fluids. The contributions …

Percolation-Induced Thermo-Rheological Transitions In Biobased Graphene Nanoplatelet Nanofluids, Abiodun A. Saka, Tobechukwu K. Abor, Anthony C. Okafor, Monday U. Okoronkwo

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Biobased lubricants with thermally responsive rheology are increasingly needed for high-efficiency mechanical systems. Graphene nanoplatelet (GnP) nanofluids in vegetable oils are promising candidates, yet their percolation-driven thermo-rheological transitions remain insufficiently understood. In particular, the coupled concentration–temperature landscape controlling network-mediated flow transitions has not previously been established. Here, surfactant-free GnP nanofluids were prepared in high-oleic soybean oil (HOSO) at concentrations of 0.025–2.15% v/v (ϕ), and their rheological response was mapped between 25°C and 80 °C under steady-shear and oscillatory conditions. Below the percolation threshold (ɸ ≤ 0.1% v/v), the nanofluids exhibit near-Newtonian behavior, moderate reversible viscosity enhancement, and classical Arrhenius-type temperature …

Laplace Factor Models In High-Dimensional Data, Siqi Liu, Xuerong Meggie Wen, Akim Adekpedjou, Guangbao Guo

Mathematics and Statistics Faculty Research & Creative Works

Laplace factor models (LFMs) provide a heavy-tailed alternative to Gaussian factor models by representing high-dimensional observations through a low-rank common component and Laplace-distributed idiosyncratic errors. This paper develops an assumption-consistent finite-sample analysis of matrix concentration, covariance estimation, and Monte Carlo integration under this model. We first formulate the model with explicit dimensional, independence, covariance, and identifiability conditions. Standard matrix Laplace-transform and matrix Bernstein inequalities are then recalled with their precise applicability conditions. Because untruncated Laplace variables are neither almost surely bounded nor strongly log-concave, these standard results cannot be applied directly in the forms commonly used for bounded or Gaussian-like …

A Stabilized Weighted Interior Penalty Method For Thermal Convection Model In Heterogeneous Porous Media, Yuanyuan Hou, Qianqian Ding, Xiaoming He, Yanping Lin

Mathematics and Statistics Faculty Research & Creative Works

In this article, we propose and analyze a stabilized weighted interior penalty method for solving the thermal convection problems in heterogeneous porous media. We first transform the thermal convection model into the pressure primal form with homogeneous Neumann boundary condition and develop a symmetric weighted interior penalty method to handle the discontinuous Darcy number and automatically adjust the penalty coefficient corresponding to the varying permeability. Then we recover the velocity by a stabilized method and incorporate it into the energy equation to obtain the temperature. The stability and convergence rates of the numerical solutions are rigorously proved and verified by …

Non-Uniform Mixing Of Quantum Walks On The Symmetric Group, Avah Banerjee

Computer Science Faculty Research & Creative Works

It is well-known that classical random walks on regular graphs converge to the uniform distribution. Quantum walks, in their various forms, are quantization's of their corresponding classical random walk processes. Gerhardt and Watrous (2003) demonstrated that continuous-time quantum walks do not converge to the uniform distribution on certain Cayley graphs of the Symmetric group, which by definition are all regular. In this paper, we demonstrate that discrete-time quantum walks, in the sense of quantized Markov chains as introduced by Szegedy (2004), also do not converge to the uniform distribution. We analyze the spectra of the Szegedy walk operators using the …

Where Do Ai Coding Agents Fail? An Empirical Study Of Failed Agentic Pull Requests In Github, Ramtin Ehsani, Sakshi Pathak, Shriya Rawal, Abdullah Al Mujahid, Mia Mohammad Imran, Preetha Chatterjee

Computer Science Faculty Research & Creative Works

AI coding agents are now submitting pull requests (PRs) to software projects, acting not just as assistants but as autonomous contributors. As these agentic contributions are rapidly increasing across real repositories, little is known about how they behave in practice and why many of them fail to be merged. In this paper, we conduct a large-scale study of 33k agent-authored PRs made by five coding agents across GitHub. (RQ1) We first quantitatively characterize merged and not-merged PRs along four broad dimensions: 1) merge outcomes across task types, 2) code changes, 3) CI build results, and 4) review dynamics. We observe …

Hybrid Microbial-Enzymatic Electrosynthesis For De Novo Decanoic Acid Production From Co2, Yongze Wang, Wenjin Dong, Meijing Wei, Su Song, Zhiqing Huang, Yaojie Liu, Shelley D. Minteer, Hui Chen

Chemistry Faculty Research & Creative Works

The steady increase in atmospheric CO₂ levels is a key driver of anthropogenic climate change, prompting widespread global concern. In this context, the sustainable conversion of CO₂ into value-added chemicals represents an attractive route toward carbon-neutral manufacturing and green chemistry. Here, we developed a proof-of-concept hybrid microbial-enzymatic electrosynthesis system that combines microbial electrosynthesis (MES) and enzymatic electrosynthesis (EES) to convert CO₂ into decanoic acid under ambient conditions, using electrical energy. In the MES module, CO₂ was converted to acetate via the Wood–Ljungdahl pathway of Clostridium ljungdahlii, using H2 produced by electrochemical water splitting as the electron …

Entity Labels Are Not Entity Signals: A Framework For Observable Relevance In Document Re-Ranking, Utshab Kumar Ghosh, Shubham Chatterjee

Computer Science Faculty Research & Creative Works

Entity-aware document retrieval uses query-associated entities as ranking signals, assuming that semantically relevant entities are also useful retrieval signals. We show this assumption is insufficient - and explain why. Unlike terms, which are ground-truth observations, entity links are hypotheses produced by an imperfect linker: an entity can be topically central yet provide no discriminative signal if the linker fires indiscriminately across relevant and non-relevant documents. We formalize this as a distinction between Conceptual Entity Relevance (CER) - whether an entity is topically related to a query - and Observable Entity Relevance (OER) - whether its observed presence in a collection …

Reproduction Beyond Benchmarks: Constbert And Colbert-V2 Across Backends And Query Distributions, Utshab Kumar Ghosh, Ashish David, Shubham Chatterjee

Computer Science Faculty Research & Creative Works

Reproducibility must validate architectural robustness, not just numerical accuracy. We evaluate ColBERT-v2 and ConstBERT across five dimensions, finding that while ConstBERT reproduces within 0.05% MRR@10 on MS-MARCO, both models show a drop of 86-97% on long, narrative queries (TREC ToT 2025). Ablations prove this failure is architectural: performance plateaus at 20 words because the MaxSim operator's uniform token weighting cannot distinguish signal from filler noise. Furthermore, undocumented backend parameters create an 8-point gap due to ConstBERT's sparse centroid coverage, and fine-tuning with 3x more data actually degrades performance by up to 29%. We conclude that architectural constraints in multi-vector retrieval …

Depro: Understanding The Role Of Llms In Debugging Competitive Programming Code, Nabiha Parvez, Md Tanvin Sarkar Pallab, Mia Mohammad Imran, Tarannum Shaila Zaman

Computer Science Faculty Research & Creative Works

Debugging consumes a substantial portion of the software development lifecycle, yet researchers do not yet understand well the effectiveness of Large Language Models (LLMs) in this task. Competitive programming offers a rich benchmark for such evaluation, given its diverse problem domains and strict efficiency requirements. We present an empirical study of LLM-based debugging on competitive programming problems and introduce DePro, a test-case-driven approach that assists programmers by correcting existing code rather than generating new solutions. DePro combines brute-force reference generation, stress testing, and iterative LLM-guided refinement to efficiently identify and resolve errors. Experiments on 13 faulty user submissions from Codeforces …

Llm-Enabled Open-Source Systems In The Wild: An Empirical Study Of Vulnerabilities In Github Security Advisories, Fariha Tanjim Shifat, Hariswar Baburaj, Ce Zhou, Jaydeb Sarker, Mia Mohammad Imran

Computer Science Faculty Research & Creative Works

Large language models (LLMs) are increasingly embedded in open-source software (OSS) ecosystems, creating complex interactions among natural language prompts, probabilistic model outputs, and execution-capable components. However, it remains unclear whether traditional vulnerability disclosure frameworks adequately capture these model-mediated risks. To investigate this, we analyze 295 GitHub Security Advisories published between January 2025 and January 2026 that reference LLM-related components, and we manually annotate a sample of 100 advisories using the OWASP Top 10 for LLM Applications 2025.We find no evidence of new implementation-level weakness classes specific to LLM systems. Most advisories map to established CWEs, particularly injection and deserialization weaknesses. …

“Innocent” Electrolytes Can Influence Organic Electrosynthetic Selectivity, Zachary A. Nguyen, Nhu H. Quach, Mayank Tanwar, Joshua A. Beeler, Kevin Mcfadden, Dylan G. Boucher, Matthew Neurock, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Organic electrosynthesis has emerged as a powerful strategy for leveraging electricity in organic synthesis. Despite its growing popularity, the fundamental molecular interactions governing electrochemical systems remain poorly understood. Many electrosynthetic reactions show strong dependence on the identity of the allegedly inert supporting electrolyte, which can significantly impact yields and selectivity, yet the physical origins of these effects are largely unexplored. A mechanistic understanding of electrolyte effects would enable more rational reaction design for applications. Here, we use cyclic voltammetry to investigate cobalt-based metal–carbon bond homolysis and elucidate how common supporting electrolytes influence reaction rates. Bulk electrolysis experiments further reveal how …

"Todo: Fix The Mess Gemini Created": Towards Understanding Genai-Induced Self-Admitted Technical Debt, Abdullah Al Mujahid, Mia Mohammad Imran

Computer Science Faculty Research & Creative Works

As large language models (LLMs) such as ChatGPT, Copilot, Claude, and Gemini become integrated into software development workflows, developers increasingly leave traces of AI involvement in their code comments. Among these, some comments explicitly acknowledge both the use of generative AI and the presence of technical shortcomings. Analyzing 6,540 LLM-referencing code comments from public Python and JavaScript-based GitHub repositories (November 2022-July 2025), we identified 81 that also self-admit technical debt (SATD). Developers most often describe postponed testing, incomplete adaptation, and limited understanding of AI-generated code, suggesting that AI assistance affects both when and why technical debt emerges. We term GenAI-Induced …

Challenges In Scaling R-Tree Spatial Search On Processing-In-Memory, Tasmia Jannat, Michael Gowanlock, Satish Puri

Computer Science Faculty Research & Creative Works

Spatial query processing is important in scientific, geospatial, and data-intensive applications. R-trees are widely used to index spatial objects, but their query-dependent traversal creates irregular work across different regions. This poster studies the challenges of scaling R-tree spatial search on a commercial Processing-in-Memory (PIM) system. Although PIM reduces CPU to memory data movement by executing search near memory, it does not remove full-pipeline overheads: the host still manages data placement, query batching, kernel launches, result retrieval, and aggregation. Our results show strong DPU-side search acceleration, with PIM kernel speedup ranging from about 20 x to 73 x, but end-to-end speedup …

Learning Programming In Informal Spaces: Using Emotion As A Lens To Understand Novice Struggles On R/Learnprogramming, Alif Al Hasan, Subarna Saha, Mia Mohammad Imran

Computer Science Faculty Research & Creative Works

Novice programmers experience emotional difficulties in informal online learning environments, where Confusion and Frustration can hinder motivation and learning outcomes. This study investigates novice programmers' emotional experiences in informal settings, identifies causes of emotional struggle, and explores design opportunities for affect-aware support systems. We manually annotated 1,500 posts from r/learnprogramming using the Learning-Centered Emotions framework, applying clustering, and axial coding. Confusion, Curiosity, and Frustration dominated emotional experiences, sometimes co-occurring and linked to early learning stages. Positive emotions were infrequent. The primary emotional triggers included ambiguous errors, unclear learning pathways, and misaligned resources. We identify five key areas where novice programmers …

Performance Evaluation Of Approximate Nearest Neighbor Search On Nvidia Bluefield-3 Dpu, Sophia Bhoria, Nathan Tibbetts, Arjun Kirubakaran, Alima Subedi, Satish Puri

Computer Science Faculty Research & Creative Works

Advanced SmartNICs known as Data Processing Units (DPU) enable in-network data analytics, being equipped with standard processors and accelerators capable of doing custom computation on-NIC. These SmartNICs are advantageous because the host CPU can delegate simpler data analytics tasks, like filtering, to the NIC where the data first arrives. Only data needing further refinement must be passed on to the host CPU. Our benchmarks focus on NVIDIA's commercially available Bluefield-3 DPU. Similarity search, particularly Approximate Nearest Neighbor (ANN) search, is an important domain with wide usage across numerous applications. We explore ANN search on SmartNICs, providing insight into the performance …