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Related Articles from SNS
Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads
new Abstract: The rapid growth of large-scale machine learning (ML) has made distributed training across multiple GPUs a fundamental component of modern ML systems. As model sizes and computational throughput continue to increase, communication overhead has become a dominant bottleneck in multi-GPU training, particularly when computation and communication are executed sequentially. This work explores concurrent execution of computation and collective communication using two portable runtime...
A Fragmentation-Aware Adaptive Bilevel Search Framework for Service Mapping in Computing Power Networks
arXiv:2507.07535v2 Announce Type: replace Abstract: Computing Power Network (CPN) unifies wide-area computing resources through coordinated network control, while cloud-native abstractions enable flexible resource orchestration and on-demand service provisioning atop the elastic infrastructure CPN provides. However, current approaches fall short of fully integrating computing resources via network-enabled coordination as envisioned by CPN. In particular, optimally mapping services to an...
Subspace-selective unitary manipulation based on the Hilbert-space symmetric structures in the multiple-quantum operator algebra spaces in the quantum-computing speedup theory
Announce Type: cross Abstract: The quantum-computing speedup theory considers the symmetric structures and properties of quantum systems as the fundamental Quantum-Computing-Speedup (QCS) resources which are responsible for exponentially speeding up quantum computing and simulating. At present a large and important problem is how to make use of the fundamental QCS resources to speed up essentially quantum computing and simulating. Here the author makes a great effort toward solving this...
Subspace-selective unitary manipulation based on the Hilbert-space symmetric structures in the multiple-quantum operator algebra spaces in the quantum-computing speedup theory
arXiv:2606.03859v2 Announce Type: replace-cross Abstract: The quantum-computing speedup theory considers the symmetric structures and properties of quantum systems as the fundamental Quantum-Computing-Speedup (QCS) resources which are responsible for exponentially speeding up quantum computing and simulating. At present a large and important problem is how to make use of the fundamental QCS resources to speed up essentially quantum computing and simulating. Here the author makes a great...
Learning to Strategically Acquire Resources in Competition
arXiv:2606.06882v1 Announce Type: new Abstract: We consider multiple agents competing to acquire some costly divisible resource (e.g. shares of a financial asset, compute resources, etc.) Leveraging a standard model for price dynamics, we propose a novel game-theoretic model for this problem, generalizing settings studied in diverse literatures. Our analysis considers different assumptions on the information available to agents.
Quantum feature-map learning with reduced resource overhead
Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically. In quantum machine learning, success hinges on quantum feature-maps, which embed classical data into the state space of qubits. We introduce Quantum Feature-Map Learning via Analytic Iterative Reconstructions (Q-FLAIR), an algorithm that reduces quantum resource overhead in iterative feature-map circuit construction.
Hyper-Dimensional Fingerprints as Molecular Representations
arXiv:2604.27810v2 Announce Type: replace Abstract: Computational molecular representations underpin virtual screening, property prediction, and materials discovery. Conventional fingerprints are efficient and deterministic but lose structural information through hash-based compression, particularly at low dimensionalities. Learned representations from graph neural networks recover this expressiveness but require task-specific training and substantial computational resources.
STEPS: Semantic-Contract-Guided Scheduling for LLM-Assisted Natural-Language-Driven Edge AI Services
arXiv:2606.09537v1 Announce Type: new Abstract: Networked AI services are increasingly delivered through edge infrastructures to support latency-sensitive applications. Edge scheduling is critical for deciding where and how AI services are executed under limited communication and computing resources. Existing frameworks usually assume that requirements are given as numerical constraints, such as latency bounds, energy budgets, or cost limits.
Distant Object Localisation from Noisy Image Segmentation Sequences
arXiv:2509.20906v3 Announce Type: replace Abstract: 3D object localisation based on a sequence of camera measurements is essential for safety-critical surveillance tasks, such as drone-based wildfire monitoring. Localisation of objects detected with a camera can typically be solved with specialised sensor configurations or 3D scene reconstruction. However, in the context of distant objects or tasks limited by the amount of available computational resources, neither solution is feasible.
Heterogeneous Decentralized Diffusion Models
Announce Type: replace Abstract: Training frontier-scale diffusion models often requires substantial computational resources concentrated in tightly-coupled clusters, limiting participation to well-resourced institutions. While Decentralized Diffusion Models (DDM) enable training multiple experts in isolation, existing approaches require 1176 GPU-days and homogeneous training objectives across all experts. We present an efficient framework that dramatically reduces resource requirements...