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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
The First OpenFOAM HPC Challenge (OHC-1)
Sergey Lesnik, Gregor Olenik, Mark Wassermann · 2026-03-29 · via cs.DC updates on arXiv.org

The first OpenFOAM HPC Challenge (OHC-1) was organised by the OpenFOAM HPC Technical Committee (HPCTC) to collect a snapshot of OpenFOAM's computational performance on contemporary production hardware and to compare hardware-constrained submissions with software-track optimisations. Participants ran a common incompressible steady-state RANS case, the open-closed cooling DrivAer (occDrivAer) configuration, on prescribed meshes, submitting either with the reference setup (hardware track) or with modified solvers, decomposition strategies, or accelerator offloading (software track). In total, 237 valid datapoints were submitted by 12 contributors: 175 in the hardware track and 62 in the software track. The hardware track covered 25 distinct CPU models across AMD, Intel, and ARM families, with runs spanning from single-node configurations up to 256 nodes (32768 CPU cores). Wall-clock times ranged from 7.8 minutes to 65.7 hours and reported energy-to-solution from 2.1 to 236.9 kWh. Analysis of the hardware track identified a Pareto front of optimal balance between time- and energy-to-solution, and revealed that on-package high-bandwidth memory (HBM) dominates single-node performance for next-generation CPUs. Software-track submissions achieved up to 28% lower energy per iteration, 17% higher maximum performance per node, and 72% shorter minimum time per iteration than the best hardware-track results, with full GPU ports and selective-memory optimisations leading the performance range. This manuscript describes the challenge organisation, the case setup and metrics, and presents the main findings from both tracks together with an outlook for future challenges.