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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
Efficient Partial Snapshot Implementations
Nikolaos D. Kallimanis, Eleni Kanellou, Charidimos Kiosterakis · 2020-06-11 · via cs.DC updates on arXiv.org

In this work, we propose the $λ$-scanner snapshot, a variation of the snapshot object, which supports any fixed amount of $0 < λ\leq n$ different $SCAN$ operations being active at any given time. Whenever $λ$ is equal to the number of processes $n$ in the system, the $λ$-scanner object implements a multi-scanner object, while in case that $λ$ is equal to $1$, the $λ$-scanner object implements a single-scanner object. We present the $λ-Snap$ snapshot object, a wait-free $λ$-scanner snapshot implementation that has a step complexity of $O(λ)$ for $UPDATE$ operations and $O(λm)$ for $SCAN$ operations. The space complexity of $λ-Snap$ is $O(λm)$. $λ-Snap$ provides a trade-off between the step/space complexity and the maximum number of $SCAN$ operations that the system can afford to be active on any given point in time. The low space complexity that our implementations provide makes them more appealing in real system applications. Moreover, we provide a slightly modified version of the $λ-Snap$ implementation, which is called partial $λ-Snap$, that is able to support dynamic partial scan operations. In such an object, processes can execute modified $SCAN$ operations called $PARTIAL\_SCAN$ that could obtain a part of the snapshot object avoiding to read the whole set of components. In this work, we first provide a simple single-scanner version of $λ-Snap$, which is called $1-Snap$. We provide $1-Snap$ just for presentation purposes, since it is simpler than $λ-Snap$. The $UPDATE$ in $1-Snap$ has a step complexity of $O(1)$, while the $SCAN$ has a step complexity of $O(m)$. This implementation uses $O(m)$ $CAS$ registers.