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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
DeFi and NFTs Hinder Blockchain Scalability
Lioba Heimbach, Quentin Kniep, Yann Vonlanthen, Roger Wattenhofe · 2023-02-14 · via cs.DC updates on arXiv.org

Many classical blockchains are known to have an embarrassingly low transaction throughput, down to Bitcoin's notorious seven transactions per second limit.Various proposals and implementations for increasing throughput emerged in the first decade of blockchain research. But how much concurrency is possible? In their early days, blockchains were mostly used for simple transfers from user to user. More recently, however, decentralized finance (DeFi) and NFT marketplaces have completely changed what is happening on blockchains. Both are built using smart contracts and have gained significant popularity. Transactions on DeFi and NFT marketplaces often interact with the same smart contracts. We believe this development has transformed blockchain usage. In our work, we perform a historical analysis of Ethereum's transaction graph. We study how much interaction between transactions there was historically and how much there is now. We find that the rise of DeFi and NFT marketplaces has led to an increase in "centralization" in the transaction graph. More transactions are now interconnected: currently there are around 200 transactions per block with 4000 interdependencies between them. We further find that the parallelizability of Ethereum's current interconnected transaction workload is limited. A speedup exceeding a factor of five is currently unrealistic.