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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
Cluster-level tuning of a shallow water equation solver o...
Andrey Vladimirov, Cliff Addison · 2014-08-08 · via cs.DC updates on arXiv.org

The paper demonstrates the optimization of the execution environment of a hybrid OpenMP+MPI computational fluid dynamics code (shallow water equation solver) on a cluster enabled with Intel Xeon Phi coprocessors. The discussion includes: (1) Controlling the number and affinity of OpenMP threads to optimize access to memory bandwidth; (2) Tuning the inter-operation of OpenMP and MPI to partition the problem for better data locality; (3) Ordering the MPI ranks in a way that directs some of the traffic into faster communication channels; (4) Using efficient peer-to-peer communication between Xeon Phi coprocessors based on the InfiniBand fabric. With tuning, the application has 90% percent efficiency of parallel scaling up to 8 Intel Xeon Phi coprocessors in 2 compute nodes. For larger problems, scalability is even better, because of the greater computation to communication ratio. However, problems of that size do not fit in the memory of one coprocessor. The performance of the solver on one Intel Xeon Phi coprocessor 7120P exceeds the performance on a dual-socket Intel Xeon E5-2697 v2 CPU by a factor of 1.6x. In a 2-node cluster with 4 coprocessors per compute node, the MIC architecture yields 5.8x more performance than the CPUs. Only one line of legacy Fortran code had to be changed in order to achieve the reported performance on the MIC architecture (not counting changes to the command-line interface). The methodology discussed in this paper is directly applicable to other bandwidth-bound stencil algorithms utilizing a hybrid OpenMP+MPI approach.