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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
Scalability of the plasma physics code GEM
Bruce D. Scott, Volker Weinberg, Olivier Hoenen, Anupam Karmakar · 2013-12-04 · via cs.DC updates on arXiv.org

We discuss a detailed weak scaling analysis of GEM, a 3D MPI-parallelised gyrofluid code used in theoretical plasma physics at the Max Planck Institute of Plasma Physics, IPP at Garching b. München, Germany. Within a PRACE Preparatory Access Project various versions of the code have been analysed on the HPC systems SuperMUC at LRZ and JUQUEEN at Jülich Supercomputing Centre (JSC) to improve the parallel scalability of the application. The diagnostic tool Scalasca has been used to filter out suboptimal routines. The code uses the electromagnetic gyrofluid model which is a superset of magnetohydrodynamic and drift-Alfvén microturbulance and also includes several relevant kinetic processes. GEM can be used with different geometries depending on the targeted use case, and has been proven to show good scalability when the computational domain is distributed amongst two dimensions. Such a distribution allows grids with sufficient size to describe small scale tokamak devices. In order to enable simulation of very large tokamaks (such as the next generation nuclear fusion device ITER in Cadarache, France) the third dimension has been parallelised and weak scaling has been achieved for significantly larger grids.