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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
Reclaiming Idle CPU Cycles on Kubernetes: Sparse-Domain M...
[Submitted on 1 Apr 2026 (v1), last revised 24 Jul 2026 (this ve · 2026-04-01 · via cs.DC updates on arXiv.org

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Abstract:When simulations parallelized with the Message Passing Interface (MPI) run on shared Kubernetes clusters, conventional CPU scheduling leaves most provisioned cycles idle at synchronization barriers. This paper presents a multiplexing framework that reclaims this idle capacity by co-locating multiple simulations on the same cluster. Profiling through the MPI profiling interface quantifies per-rank idle fractions; proportional CPU allocation then lets a second simulation run concurrently with minimal overhead, yielding 1.77x throughput. A sweep to eight concurrent simulations locates the capacity boundary: throughput rises to 4.09x at N=6, where pod count equals cluster vCPU count, then collapses to 0.50x at N=8 as busy-wait MPI progress threads displace useful computation. A single-parameter analytical model fitted on one mesh reproduces all twelve concurrent configurations, measured across a 4x range of mesh sizes, within 6.4%, including the boundary point within 0.8%. Four NAS Parallel Benchmarks kernels (1.92-1.99x at N=2), a half-size cluster (1.68x), and per-pod memory accounting confirm generality across workloads, cluster sizes, and resource dimensions. A dynamic controller automates profiling, In-Place Pod Vertical Scaling (KEP-1287), packing, and fairness monitoring, achieving 3.25x throughput for four simulations with zero pod restarts, the first CPU application of in-place resize to running MPI processes.

Submission history

From: Tianfang Xie [view email]
[v1] Wed, 1 Apr 2026 02:03:23 UTC (427 KB)
[v2] Fri, 24 Jul 2026 00:20:33 UTC (7,713 KB)