PromptTuner: SLO-Aware Elastic System for LLM Prompt Tuning
arXiv:2603.05087 · Scholar 收录
DAWN: Dependency-Aware Fast Inference for Diffusion LLMs
arXiv:2602.06953 · Scholar 收录
Inference-time Alignment via Sparse Junction Steering
arXiv:2602.21215 · Scholar 收录
Semantic-Aware Scheduling for GPU Clusters with Large Language Models
arXiv:2510.03334 · Scholar 收录
DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving
arXiv:2506.13497 · Scholar 收录
UniTG: A Unified System for Efficient and Seamless Textual Graph Learning
International Conference on Very Large Data Bases (VLDB), August, 2026
尚未找到公开 arXiv 或全文；当前只保存作者摘要页。
SpecForge: A Flexible and Efficient Open-Source Training Framework for Speculative Decoding
International Conference on Machine Learning (ICML), July, 2026
CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control
International Conference on Machine Learning (ICML), July, 2026
DSB: Dynamic Sliding Block Scheduling for Diffusion LLMs
International Conference on Machine Learning (ICML), July, 2026
SPPO: Making Million-Token LLM Training Practical on Modest GPU Clusters
ACM International Conference on Supercomputing (ICS), July, 2026
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
Di-PS: System-Algorithm Co-Design for Asynchronous and Heterogeneous Cross-cluster LLM Training at Scale
USENIX Symposium on Networked Systems Design and Implementation (NSDI), May, 2026
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
ReSpec: Towards Optimizing Speculative Decoding in Reinforcement Learning Systems
Annual Conference on Machine Learning and Systems (MLSys), May, 2026
DSA: Efficient Inference For Video Generation Models via Distributed Sparse Attention
International Conference on Learning Representations (ICLR), April, 2026
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
Impact-driven Context Filtering For Cross-file Code Completion
Conference on Language Modeling (COLM), October, 2025
Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving
Annual Conference on Machine Learning and Systems (MLSys), May, 2025
TorchGT: A Holistic System for Large-scale Graph Transformer Training
International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), November, 2024
Lins: Reducing Communication Overhead of ZeRO for Efficient LLM Training
IEEE/ACM International Symposium on Quality of Service (IWQoS), June, 2024
Ymir: A Scheduler for Foundation Model Fine-tuning Workloads in Datacenters
ACM International Conference on Supercomputing (ICS), June, 2024
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
AutoSched: An Adaptive Self-configured Framework for Scheduling Deep Learning Training Workloads
ACM International Conference on Supercomputing (ICS), June, 2024
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
Sylvie: 3D-adaptive and Universal System for Large-scale Graph Neural Network Training
IEEE International Conference on Data Engineering (ICDE), May, 2024
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
Characterization of Large Language Model Development in the Datacenter
USENIX Symposium on Networked Systems Design and Implementation (NSDI), April, 2024
Hydro: Surrogate-based Hyperparameter Tuning Service in Datacenters
USENIX Symposium on Operating Systems Design and Implementation (OSDI), July, 2023
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
Lucid: A Non-Intrusive, Scalable and Interpretable Scheduler for Deep Learning Training Jobs
ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Distinguished Paper Award , March, 2023
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
Titan: A Scheduler for Foundation Model Fine-tuning Workloads
ACM Symposium on Cloud Computing (SoCC), November, 2022
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
Tear Up the Bubble Boom: Lessons Learned From a Deep Learning Research and Development Cluster
IEEE International Conference on Computer Design (ICCD), October, 2022
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
CHRONUS: A Novel Deadline-aware Scheduler for Deep Learning Training Jobs
ACM Symposium on Cloud Computing (SoCC), November, 2021
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters
International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), November, 2021
arXiv 原生 HTML 不可用，改用 ar5iv 正文镜像。
Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
Accepted by ACM Computing Surveys
ICEFROG: A Layer-Elastic Scheduling System for Deep Learning Training in GPU Clusters
IEEE Transactions on Parallel and Distributed Systems, Volume: 36, Issue: 6, June 2025
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
UniSched: A Unified Scheduler for Deep Learning Training Jobs with Different User Demands
IEEE Transactions on Computers, Volume: 73, Issue: 6, June 2024
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
Deep Learning Workload Scheduling in GPU Datacenters: A Survey
ACM Computing Surveys, Volume: 56, Issue: 6, January 2024
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。
ASTRAEA: A Fair Deep Learning Scheduler for Multi-tenant GPU Clusters
IEEE Transactions on Parallel and Distributed Systems, Volume: 33, Issue: 11, November 2022
未找到 arXiv HTML；由作者公开 PDF 转为本地 HTML。