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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
PhoenixOS: Concurrent OS-level GPU Checkpoint and Restore...
Xingda Wei, Zhuobin Huang, Tianle Sun, Yingyi Hao, Rong Chen, Mi · 2024-05-20 · via cs.DC updates on arXiv.org

PHOENIXOS (PHOS) is the first OS service that can concurrently checkpoint and restore (C/R) GPU processes--a fundamental capability for critical tasks such as fault tolerance, process migration, and fast startup. While concurrent C/R is well-established on CPUs, it poses unique challenges on GPUs due to their lack of essential features for efficiently tracing concurrent memory reads and writes, such as specific hardware capabilities (e.g., dirty bits) and OS-mediated data paths (e.g., copy-on-write). To ensure correct concurrent C/R, PHOS proactively detects GPU memory reads and writes through a two-step process: first, it speculates about GPU memory accesses based on the arguments used when launching GPU kernels; then, it validates these accesses efficiently at runtime using binary instrumentation. With this validated speculation, PHOS retrofits CPU-based concurrent C/R for GPUs through software-based approaches, including soft copy-on-write, soft recopy, and soft on-demand restore. PHOS further proposes several GPU-aware techniques for efficient GPU C/R, including coordinated checkpoint data transfer and execution context pool. For downstream tasks that use C/R for tolerating failures, migrating processes between machines, and accelerating cold starts in serverless computing, PHOS achieves orders of magnitude higher performance than state-of-the-art OS-level GPU C/R systems like NVIDIA cuda-checkpoint.