惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

量子位
Stack Overflow Blog
Stack Overflow Blog
人人都是产品经理
人人都是产品经理
The GitHub Blog
The GitHub Blog
Engineering at Meta
Engineering at Meta
Vercel News
Vercel News
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Y
Y Combinator Blog
The Cloudflare Blog
Last Week in AI
Last Week in AI
B
Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
T
Tailwind CSS Blog
V
Visual Studio Blog
博客园 - 三生石上(FineUI控件)
小众软件
小众软件
Google DeepMind News
Google DeepMind News
D
DataBreaches.Net
博客园 - 司徒正美
B
Blog RSS Feed
Microsoft Azure Blog
Microsoft Azure Blog
罗磊的独立博客
Hugging Face - Blog
Hugging Face - Blog
L
LangChain Blog

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
Dynamic Load Balancing for Uncertainty Quantification wit...
[Submitted on 24 Jun 2026] · 2026-06-25 · via cs.DC updates on arXiv.org

View PDF

Abstract:Uncertainty Quantification (UQ) workflows present a particular scheduling challenge in high performance computing environments, as they typically generate large numbers of heterogeneous model evaluations with loose but non-trivial dependencies between tasks. A static one-size-fits-all approach in traditional schedulers is inadequate to handle heterogeneous tasks optimally. We introduce an improved load balancer in the UQ and Modelling Bridge (UM-Bridge) framework aimed at mitigating these issues; UM-Bridge is a language-agnostic interface developed to couple UQ software with numerical simulation. As a realistic example, we test the load balancer with a Bayesian inverse problem solved via multilevel delayed acceptance sampling. The underlying forward problem is a hierarchy of tsunami simulations enabled through ExaHyPE, whose runtimes span several orders of magnitude and loose dependencies between levels make the workload particularly challenging to schedule. Our results indicate the load balancer is effective at distributing the sampling requests with an average node idle time of close to a millisecond, while not making any prior assumptions about the workload.

Submission history

From: Chung Ming Loi [view email]
[v1] Wed, 24 Jun 2026 11:02:47 UTC (13,850 KB)