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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
Achieving Starvation-Freedom with Greater Concurrency in ...
Chirag Juyal, Sandeep Kulkarni, Sweta Kumari, Sathya Peri, Archi · 2019-04-08 · via cs.DC updates on arXiv.org

To utilize the multi-core processors properly concurrent programming is needed. Concurrency control is the main challenge while designing a correct and efficient concurrent program. Software Transactional Memory Systems (STMs) provides ease of multithreading to the programmer without worrying about concurrency issues such as deadlock, livelock, priority inversion, etc. Most of the STMs works on read-write operations known as RWSTMs. Some STMs work at high-level operations and ensure greater concurrency than RWSTMs. Such STMs are known as Object-Based STMs (OSTMs). The transactions of OSTMs can return commit or abort. Aborted OSTMs transactions retry. But in the current setting of OSTMs, transactions may starve. So, we proposed a Starvation-Free OSTM (SF-OSTM) which ensures starvation-freedom in object based STM systems while satisfying the correctness criteria as co-opacity. Databases, RWSTMs and OSTMs say that maintaining multiple versions corresponding to each key of transaction reduces the number of aborts and improves the throughput. So, to achieve greater concurrency, we proposed Starvation-Free Multi-Version OSTM (SF-MVOSTM) which ensures starvation-freedom while storing multiple versions corresponding to each key and satisfies the correctness criteria such as local opacity. To show the performance benefits, We implemented three variants of SF-MVOSTM (SF-MVOSTM, SF-MVOSTM-GC and SF-KOSTM) and compared it with state-of-the-art STMs.