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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
Proceedings of the 2015 International Workshop on the Lus...
Neena Imam, Michael Brim, Sarp Oral · 2015-06-17 · via cs.DC updates on arXiv.org

The Lustre parallel file system has been widely adopted by high-performance computing (HPC) centers as an effective system for managing large-scale storage resources. Lustre achieves unprecedented aggregate performance by parallelizing I/O over file system clients and storage targets at extreme scales. Today, 7 out of 10 fastest supercomputers in the world use Lustre for high-performance storage. To date, Lustre development has focused on improving the performance and scalability of large-scale scientific workloads. In particular, large-scale checkpoint storage and retrieval, which is characterized by bursty I/O from coordinated parallel clients, has been the primary driver of Lustre development over the last decade. With the advent of extreme scale computing and Big Data computing, many HPC centers are seeing increased user interest in running diverse workloads that place new demands on Lustre. In March 2015, the International Workshop on the Lustre Ecosystem: Challenges and Opportunities was held in Annapolis, Maryland at the Historic Inns of Annapolis Governor Calvert House. This workshop series is intended to help explore improvements in the performance and flexibility of Lustre for supporting diverse application workloads. The 2015 workshop was the inaugural edition, and the goal was to initiate a discussion on the open challenges associated with enhancing Lustre for diverse applications, the technological advances necessary, and the associated impacts to the Lustre ecosystem. The workshop program featured a day of tutorials and a day of technical paper presentations.