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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
Ibdxnet: Leveraging InfiniBand in Highly Concurrent Java ...
Stefan Nothaas, Kevin Beineke, Michael Schoettner · 2018-12-05 · via cs.DC updates on arXiv.org

In this report, we describe the design and implementation of Ibdxnet, a low-latency and high-throughput transport providing the benefits of InfiniBand networks to Java applications. Ibdxnet is part of the Java-based DXNet library, a highly concurrent and simple to use messaging stack with transparent serialization of messaging objects and focus on very small messages (< 64 bytes). Ibdxnet implements the transport interface of DXNet in Java and a custom C++ library in native space using JNI. Several optimizations in both spaces minimize context switching overhead between Java and C++ and are not burdening message latency or throughput. Communication is implemented using the messaging verbs of the ibverbs library complemented by an automatic connection management in the native library. We compared DXNet with the Ibdxnet transport to the MPI implementations FastMPJ and MVAPICH2. For small messages up to 64 bytes using multiple threads, DXNet with the Ibdxnet transport achieves a bi-directional message rate of 10 million messages per second and surpasses FastMPJ by a factor of 4 and MVAPICH by a factor of 2. Furthermore, DXNet scales well on a high load all-to-all communication with up to 8 nodes achieving a total aggregated message rate of 43.4 million messages per second for small messages and a throughput saturation of 33.6 GB/s with only 2 kb message size.