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cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
HPC-vQPU: A Service-Export Architecture for Virtual QPUs ...
Shusen Liu, Pascal Jahan Elahi, Ugo Varetto · 2026-05-14 · via cs.DC updates on arXiv.org

Device-aware quantum simulation increasingly requires HPC-scale accelerators, yet secure supercomputers expose batch-scheduled execution environments rather than the interactive, backend-oriented interfaces expected by quantum software. The key obstacle is not only remote job submission: an HPC-hosted virtual QPU must preserve topology, native-gate, and calibration semantics across queue delay, scheduler allocation, compute-node isolation, and partial execution-side failures, without opening inbound paths into the cluster. We present HPC-vQPU, a service-export architecture for virtual QPUs on batch-scheduled HPC systems. HPC-vQPU separates a cloud-facing control plane, which owns device identity, task lifecycle, snapshot binding, and event projection, from an HPC-resident execution plane, which claims work and realises it through scheduler-backed GPU jobs. Coordination is exclusively outbound and agent initiated. The central abstraction is a topology- and calibration-aware device snapshot bound atomically at claim time and carried into execution as an immutable contract, making each scheduled job hermetic while preserving fresh device semantics. We implement HPC-vQPU at the Pawsey Supercomputing Research Centre using Setonix GPUs, Qiskit-Aer/cuQuantum, and IBM Fez calibration data. Production experiments show that service overhead is bounded and additive, while workload scaling remains confined to the simulator; calibration-bearing snapshots produce measurable output shifts; claim-time binding prevents stale execution after pre-claim device mutation; concurrent agents complete 50/50 tasks exactly once; and explicit recovery restores stale running tasks after agent failure. These results show that secure, scheduler-mediated HPC infrastructure can export device-faithful quantum simulation as an interactive virtual-QPU service.