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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
Development of Massively Parallel Near Peak Performance S...
2022-07-19 · via cs.DC updates on arXiv.org

We address in this thesis the current need to design new parallel algorithms and tools that ease the development of geodynamic modelling applications that are suited for today's and tomorrow's hardware. We present (1) the MATLAB HPC compiler HPC.m, which greatly simplifies the building of parallel high performance applications and (2) parallel algorithms for the 3D simulation of strongly nonlinear processes as mechanical and reactive porosity waves. To simulate mechanical porosity waves we employ a massively parallel algorithm that permits to resolve the deformation of fluid-filled viscoelastic porous media in 3D. The utilized mathematical model is based on Biot's poroelastic theory, extended to account for viscous deformation and plastic yielding. The modelling results exhibit the impact of decompaction weakening on the formation of three-dimensional solitary-wave-like moving porosity channels. To simulate reactive porosity waves we use a solver for 3D deformation of fluid-filled reactive viscous porous media. The Damköhler number (Da) of the simulations is varied in order to estimate the respective roles of viscous deformation (low Da) and reaction (high Da) on wave propagation. 3D waves are found to propagate independently of their source at constant speed by going through each other for all the investigated Da. Soliton-like wave propagation as a result of metamorphic reaction provides an efficient mechanism for fluid flow in the Earth's crust. We illustrate the great performance and versatility of HPC.m by deploying it to generate solvers for a variety of physics across multiple Earth Science disciplines. All solvers run close to hardware's peak performance and were shown to scale linearly on a institute cluster with 80 GPUs. Moreover, our nonlinear poroviscoelastic two-phase flow solver scales close to ideally on Piz Daint's 5000 GPUs at the Swiss National Supercomputing Centre.