Latency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rachmut, Ben, Zivan, Roie, Yeoh, William
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Using Sequential Runtime Distributions for the Parallel Speedup Prediction of SAT Local Search
by: Arbelaez, Alejandro, et al.
Published: (2024)

OptPipe: Memory- and Scheduling-Optimized Pipeline Parallelism for LLM Training
by: Li, Hongpei, et al.
Published: (2025)

Delay-Aware Multi-Stage Edge Server Upgrade with Budget Constraint
by: Wihidayat, Endar Suprih, et al.
Published: (2025)

ELANA: A Simple Energy and Latency Analyzer for LLMs
by: Chiang, Hung-Yueh, et al.
Published: (2025)

Large Language Model Partitioning for Low-Latency Inference at the Edge
by: Kafetzis, Dimitrios, et al.
Published: (2025)

Compass: A Decentralized Scheduler for Latency-Sensitive ML Workflows
by: Yang, Yuting, et al.
Published: (2024)

Speculative Decoding in Decentralized LLM Inference: Turning Communication Latency into Computation Throughput
by: Song, Jingwei, et al.
Published: (2025)

On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance
by: Singh, Jaskirat, et al.
Published: (2024)

StreamServe: Adaptive Speculative Flows for Low-Latency Disaggregated LLM Serving
by: Kumar, Satyam, et al.
Published: (2026)

Dora: QoE-Aware Hybrid Parallelism for Distributed Edge AI
by: Jin, Jianli, et al.
Published: (2025)

AI Inference as Relocatable Electricity Demand: A Latency-Constrained Energy-Geography Framework
by: Luo, Xubin, et al.
Published: (2026)

SwizzlePerf: Hardware-Aware LLMs for GPU Kernel Performance Optimization
by: Tschand, Arya, et al.
Published: (2025)

WORKSWORLD: A Domain for Integrated Numeric Planning and Scheduling of Distributed Pipelined Workflows
by: Paul, Taylor, et al.
Published: (2026)

Mesh-Attention: A New Communication-Efficient Distributed Attention with Improved Data Locality
by: Chen, Sirui, et al.
Published: (2025)

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors
by: Los, Denis, et al.
Published: (2025)

PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System
by: Rhyner, Steve, et al.
Published: (2024)

Speeding up Local Optimization in Vehicle Routing with Tensor-based GPU Acceleration
by: Lei, Zhenyu, et al.
Published: (2025)

EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models
by: Cheng, Jialiang, et al.
Published: (2024)

High-Dimensional Data Processing: Benchmarking Machine Learning and Deep Learning Architectures in Local and Distributed Environments
by: Rodriguez, Julian, et al.
Published: (2025)

Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing
by: Anderson, Derek, et al.
Published: (2026)

FlowKV: A Disaggregated Inference Framework with Low-Latency KV Cache Transfer and Load-Aware Scheduling
by: Li, Weiqing, et al.
Published: (2025)

Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization
by: Zhu, Zhanda, et al.
Published: (2025)

Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
by: Firoz, Jesun, et al.
Published: (2025)

Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems
by: Sgambati, Matthew, et al.
Published: (2025)

Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU
by: Spoczynski, Marcin, et al.
Published: (2026)

Prompt-Aware Scheduling for Low-Latency LLM Serving
by: Tao, Yiheng, et al.
Published: (2025)

Explainable Distributed Constraint Optimization Problems
by: Rachmut, Ben, et al.
Published: (2025)

Lightweight Trustworthy Distributed Clustering
by: Li, Hongyang, et al.
Published: (2025)

Astra: Efficient and Money-saving Automatic Parallel Strategies Search on Heterogeneous GPUs
by: Wang, Peiran, et al.
Published: (2025)

MoE-Lens: Towards the Hardware Limit of High-Throughput MoE LLM Serving Under Resource Constraints
by: Yuan, Yichao, et al.
Published: (2025)

Distributed Neural Representation for Reactive in situ Visualization
by: Wu, Qi, et al.
Published: (2023)

Demystifying the Communication Characteristics for Distributed Transformer Models
by: Anthony, Quentin, et al.
Published: (2024)

FairBatching: Fairness-Aware Batch Formation for LLM Inference
by: Lyu, Hongtao, et al.
Published: (2025)

LAPS: A Length-Aware-Prefill LLM Serving System
by: She, Jianshu, et al.
Published: (2026)

PALS: Power-Aware LLM Serving for Mixture-of-Experts Models
by: Hankendi, Can, et al.
Published: (2026)

A Parallel CPU-GPU Framework for Batching Heuristic Operations in Depth-First Heuristic Search
by: Futuhi, Ehsan, et al.
Published: (2025)

Opt-GPTQ: An Optimized GPTQ Combining Sparse Attention and Quantization Techniques
by: Kong, Jie, et al.
Published: (2025)

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
by: Goyal, Harshit
Published: (2025)

TinyServe: Query-Aware Cache Selection for Efficient LLM Serving
by: Liu, Dong, et al.
Published: (2025)

TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms
by: Stojkovic, Jovan, et al.
Published: (2025)