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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
Attempt to Salvage Multi-million Dollars of Ill-conceived...
Marek Michalewicz · 2023-05-03 · via cs.DC updates on arXiv.org

In 2015 the Interdisciplinary Centre for Mathematical and Computational Modelling (ICM), University of Warsaw built a modern datacenter and installed three substantial HPC systems as part of a 168 M PLN (36 M Euro) OCEAN project. Some of the systems were ill-conceived, badly architected and for the five years of their life span have brought minimal ROI. This paper reports on a two-year intensive effort to reengineer two of these HPC systems into a hybrid, multi-cloud solution called A-CHOICeM (Akademicka CHmura Obliczeniowa ICM). The intention was to expand the user base of ICM typical HPC system from around 200 to 500 to about 100,000 potential general academic users from all institutes of higher learning in the Warsaw area. The main characteristics of this solution are integration of on-premises ICM Cloud with several public cloud providers, building solution tailored to particular groups of academic users, containerization, integration of special computational paradigms like AI and Quantum Computing. Full process of designing the solution, competitive dialogue with suppliers, and full final specifications for the solution are presented. Several roadblocks, pitfalls and difficulties encountered along the way, including the conservative attitude of "the old school" HPC admins, University bureaucracy, national funding policies and others are presented.