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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
Streaming Temporal Graphs: Subgraph Matching
Eric L. Goodman, Dirk Grunwald · 2020-04-01 · via cs.DC updates on arXiv.org

We investigate solutions to subgraph matching within a temporal stream of data. We present a high-level language for describing temporal subgraphs of interest, the Streaming Analytics Language (SAL). SAL programs are translated into C++ code that is run in parallel on a cluster. We call this implementation of SAL the Streaming Analytics Machine (SAM). SAL programs are succinct, requiring about 20 times fewer lines of code than using the SAM library directly, or writing an implementation using Apache Flink. To benchmark SAM we calculate finding temporal triangles within streaming netflow data. Also, we compare SAM to an implementation written for Flink. We find that SAM is able to scale to 128 nodes or 2560 cores, while Apache Flink has max throughput with 32 nodes and degrades thereafter. Apache Flink has an advantage when triangles are rare, with max aggregate throughput for Flink at 32 nodes greater than the max achievable rate of SAM. In our experiments, when triangle occurrence was faster than five per second per node, SAM performed better. Both frameworks may miss results due to latencies in network communication. SAM consistently reported an average of 93.7% of expected results while Flink decreases from 83.7% to 52.1% as we increase to the maximum size of the cluster. Overall, SAM can obtain rates of 91.8 billion netflows per day.