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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
Using CAS to Manage Role-Based VO Sub-Groups
Craig E. Tull, Shane Canon, Steve Chan, Doug Olson, Laura Pearlm · 2003-06-16 · via cs.DC updates on arXiv.org

LHC-era HENP experiments will generate unprecidented volumes of data and require commensurately large compute resources. These resources are larger than can be marshalled at any one site within the community. Production reconstruction, analysis, and simulation will need to take maximum advantage of these distributed computing and storage resources using the new capabilities offered by the Grid computing paradigm. Since large-scale, coordinated Grid computing involves user access across many Regional Centers and national and funding boundaries, one of the most crucial aspects of Grid computing is that of user authentication and authorization. While projects such as the DOE Grids CA have gone a long way to solving the problem of distributed authentication, the authorization problem is still largely open. We have developed and tested a prototype VO-Role management system using the Community Authorization Service (CAS) from the Globus project. CAS allows for a flexible definition of resources. In this protoype we define a role as a resource within the CAS database and assign individuals in the VO access to that resource to indicate their ability to assert the role. The access of an individual to this VO-Role resource is then an annotation of the user's CAS proxy certificate. This annotation is then used by the local resource managers to authorize access to local compute and storage resources at a granularity which is base on neither VOs nor individuals. We report here on the configuration details for the CAS database and the Globus Gatekeeper and on how this general approch could be formalized and extended to meet the clear needs of LHC experiments using the Grid.