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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
An Efficient Framework for Optimistic Concurrent Executio...
Parwat Singh Anjana, Sweta Kumari, Sathya Peri, Sachin Rathor, A · 2018-09-05 · via cs.DC updates on arXiv.org

Blockchain platforms such as Ethereum and several others execute complex transactions in blocks through user-defined scripts known as smart contracts. To append a correct block into blockchain, miners execute these transactions of smart contracts sequentially. Later the validators serially re-execute the smart contract transactions of the block to validate it. If validation is successful then the block is added to the blockchain using a consensus protocol and miner gets the incentive. In the current era of multi-core processors, by employing the serial execution of the transactions, the miners and validators fail to utilize the cores properly and as a result, have poor throughput. By entitling concurrency to smart contracts execution, we can achieve better efficiency and higher throughput. In this proposal, we develop a novel and efficient technique to execute the smart contract transactions concurrently by miner using optimistic Software Transactional Memory systems (STMs). The miner proposes a block which consists of the set of transactions, conflict graph, hash of previous block and final state of each shared data-objects. Later, we propose concurrent validator which re-executes the same smart contracts concurrently and deterministically with the help of conflict graph given by miner and verifies the final states. On successful validation, proposed block appended into the blockchain and miner gets the incentive. We consider a benchmarks from solidity documentation. We execute the smart contract transactions concurrently using Basic Time stamp Ordering (BTO) and Multi-Version Time stamp Ordering (MVTO) protocols as optimistic STMs. BTO and MVTO miner achieves 3.6x and 3.7x average speedups over serial miner respectively. BTO and MVTO validator outperform average 40.8x and 47.1x than serial validator respectively.