Abhinaba Chakraborty | Searcharxiv

arXiv2026

Profiling Concurrent Vision Inference Workloads on NVIDIA Jetson -- Extended

The proliferation of IoT devices and advancements in network technologies have intensified the demand for real-time data processing at the network edge. To address these demands, low-power AI accelerators, particularly GPUs, are increasingly deployed for inference tasks, enabling efficient computation while mitigating the latency and bandwidth limitations of cloud-based systems. Despite their growing deployment, GPUs remain underutilised even in computationally intensive workloads. This underutilisation stems from the limited understanding of GPU resource sharing, particularly in edge computing scenarios. In this work, we conduct a detailed analysis of both high- and low-level metrics, including GPU utilisation, memory usage, streaming multiprocessor (SM) utilisation, and tensor core usage, to identify bottlenecks and guide hardware-aware optimisations. By integrating traces from multiple profiling tools, we provide a comprehensive view of resource behaviour on NVIDIA Jetson edge devices under concurrent vision inference workloads. Our findings indicate that while GPU utilisation can reach $100\%$ with specific optimisations, critical low-level resources, such as SMs and tensor cores, often operate at only $15\%$ to $30\%$ utilisation. Moreover, we observe that certain CPU-side events, such as thread scheduling and context switching, frequently become bottlenecks, further constraining overall GPU performance. We provide several key observations for users of vision inference workloads on NVIDIA edge devices.

cs.DC↗

arXiv2026

A Group-Based Resource Allocation Model for the Fractional Knapsack Problem

To solve the fractional knapsack problem, Dantzig's greedy rule orders items according to their value-to-cost ratio. This ordering introduces priority issues. An arbitrarily small perturbation to the input can change the allocation if the budget is exhausted between two items with very similar ratios. To mitigate that problem, we introduce a two-stage rule. We group items sharing attributes within a radius $δ$. These groups are then evaluated in descending order of ratio, and divide their group's budget share without further ranking. Consider a group featuring an aggregate capacity $U_G$, unit costs contained in $[w^-,w^+]$, and a representative value $\widehat{v}$. The group's loss compared to the exact optimum is bounded by $\widehat{v}\, U_G\frac{w^+-w^-}{w^++w^-}+\varepsilon_v U_G$, in which $\varepsilon_v$ limits the group's internal value variation. Moreover, for any group size, this harmonic factor remains tight. The overall loss becomes restricted to the single budget-binding group whenever the grouping remains order-compatible; thus, groups containing at most $K$ items suffer a per-item loss of $\mathcal{O}(\frac{K}{n})$. Should group ratio intervals exhibit an overlap of at most $ω$, an additive term $ωC$ degrades this bound. Within the separation margin between adjacent groups, the grouped allocation remains Lipschitz continuous with respect to cost data, exhibiting a modulus of $\frac{K}{w_{\min}}$. Computing this allocation takes $\mathcal{O}(n+m\log m+|Γ|\log|Γ|)$ time given $m$ groups and a boundary group $Γ$. Alternatively, the time complexity drops to $\mathcal{O}(n+m\log m)$ if a linear-time selection method identifies the boundary group's allocation.

cs.DS↗