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MLSys 2025

N

Conference on Machine Learning and Systems

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61
accepted papers
384
authors
May 13-16, 2025
dates
USA
location

61 / 61 papers

  1. 1
    A Bring-Your-Own-Model Approach for ML-Driven Storage Placement in Warehouse-Scale Computers
    Chenxi Yang
    ,
    Yan Li
    ,
    Martin Maas
    ,
    Mustafa Uysal
    ,
    Ubaid Ullah Hafeez
    ,
    Arif Merchant
    ,
    Richard McDougall
  2. 2
    AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Engine
    Carlo Siebenschuh
    ,
    Kyle Hippe
    ,
    Ozan Gökdemir
    ,
    Alexander Brace
    ,
    Arham Mushtaq Khan
    ,
    Khalid Hossain
    ,
    Yadu N. Babuji
    ,
    Nicholas Chia
    ,
    Venkatram Vishwanath
    ,
    Arvind Ramanathan
    ,
    Rick L. Stevens
    ,
    Ian T. Foster
    ,
    Robert Underwood
  3. 3
    AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution
    Zhiqiang Xie
    ,
    Hao Kang
    ,
    Ying Sheng
    ,
    Tushar Krishna
    ,
    Kayvon Fatahalian
    ,
    Christos Kozyrakis
  4. 4
    AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds
    Yinfang Chen
    ,
    Manish Shetty
    ,
    Gagan Somashekar
    ,
    Minghua Ma
    ,
    Yogesh Simmhan
    ,
    Jonathan Mace
    ,
    Chetan Bansal
    ,
    Rujia Wang
    ,
    Saravan Rajmohan
  5. 5
    APOLLO: SGD-like Memory, AdamW-level Performance
    Hanqing Zhu
    ,
    Zhenyu Zhang
    ,
    Wenyan Cong
    ,
    Xi Liu
    ,
    Sem Park
    ,
    Vikas Chandra
    ,
    Bo Long
    ,
    David Z. Pan
    ,
    Zhangyang Wang
    ,
    Jinwon Lee
  6. 6
    Balancing Pipeline Parallelism with Vocabulary Parallelism
    Man Tsung Yeung
    ,
    Penghui Qi
    ,
    Min Lin
    ,
    Xinyi Wan
  7. 7
    COMET: Fine-grained Computation-communication Overlapping for Mixture-of-Experts
    Shulai Zhang
    ,
    Ningxin Zheng
    ,
    Haibin Lin
    ,
    Ziheng Jiang
    ,
    Wenlei Bao
    ,
    Chengquan Jiang
    ,
    Qi Hou
    ,
    Weihao Cui
    ,
    Size Zheng
    ,
    Li-Wen Chang
    ,
    Quan Chen
    ,
    Xin Liu
  8. 8
    Context Parallelism for Scalable Million-Token Inference
    Amy Yang
    ,
    Jingyi Yang
    ,
    Aya Ibrahim
    ,
    Xinfeng Xie
    ,
    Bangsheng Tang
    ,
    Grigory Sizov
    ,
    Jongsoo Park
    ,
    Jianyu Huang
  9. 9
    DiffServe: Efficiently Serving Text-to-Image Diffusion Models with Query-Aware Model Scaling
    Sohaib Ahmad
    ,
    Qizheng Yang
    ,
    Haoliang Wang
    ,
    Ramesh K. Sitaraman
    ,
    Hui Guan
  10. 10
    Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking
    Marco Federici
    ,
    Davide Belli
    ,
    Mart van Baalen
    ,
    Amir Jalalirad
    ,
    Andrii Skliar
    ,
    Bence Major
    ,
    Markus Nagel
    ,
    Paul N. Whatmough
  11. 11
    Efficient On-Device Machine Learning with a Biologically-Plausible Forward-Only Algorithm
    Baichuan Huang
    ,
    Amir Aminifar
  12. 12
    Enabling Unstructured Sparse Acceleration on Structured Sparse Accelerators
    Geonhwa Jeong
    ,
    Po-An Tsai
    ,
    Abhimanyu Rajeshkumar Bambhaniya
    ,
    Stephen W. Keckler
    ,
    Tushar Krishna
  13. 13
    FastTree: Optimizing Attention Kernel and Runtime for Tree-Structured LLM Inference
    Zaifeng Pan
    ,
    Yitong Ding
    ,
    Yue Guan
    ,
    Zheng Wang
    ,
    Zhongkai Yu
    ,
    Xulong Tang
    ,
    Yida Wang
    ,
    Yufei Ding
  14. 14
    FedProphet: Memory-Efficient Federated Adversarial Training via Robust and Consistent Cascade Learning
    Minxue Tang
    ,
    Yitu Wang
    ,
    Jingyang Zhang
    ,
    Louis DiValentin
    ,
    Aolin Ding
    ,
    Amin Hass
    ,
    Yiran Chen
    ,
    Hai Li
  15. 15
    FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
    Zihao Ye
    ,
    Lequn Chen
    ,
    Ruihang Lai
    ,
    Wuwei Lin
    ,
    Yineng Zhang
    ,
    Stephanie Wang
    ,
    Tianqi Chen
    ,
    Baris Kasikci
    ,
    Vinod Grover
    ,
    Arvind Krishnamurthy
    ,
    Luis Ceze
  16. 16
    FlexAttention: A Programming Model for Generating Fused Attention Variants
    Juechu Dong
    ,
    Boyuan Feng
    ,
    Driss Guessous
    ,
    Yanbo Liang
    ,
    Horace He
  17. 17
    FlexInfer: Flexible LLM Inference with CPU Computations
    Seonjin Na
    ,
    Geonhwa Jeong
    ,
    Byung Hoon Ahn
    ,
    Aaron Jezghani
    ,
    Jeffrey Young
    ,
    Christopher J. Hughes
    ,
    Tushar Krishna
    ,
    Hyesoon Kim
  18. 18
    FLStore: Efficient Federated Learning Storage for non-training workloads
    Ahmad Faraz Khan
    ,
    Samuel Fountain
    ,
    Ahmed M. Abdelmoniem
    ,
    Ali Raza Butt
    ,
    Ali Anwar
  19. 19
    Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs
    Zichao Yue
    ,
    Chenhui Deng
    ,
    Zhiru Zhang
  20. 20
    HyC-LoRA: Memory Efficient LoRA Fine-tuning with Hybrid Activation Compression
    Yujin Wang
    ,
    Shunan Dong
    ,
    Zongle Huang
    ,
    Yichen You
    ,
    Liu He
    ,
    Huazhong Yang
    ,
    Yongpan Liu
    ,
    Hongyang Jia
  21. 21
    Interference-aware Edge Runtime Prediction with Conformal Matrix Completion
    Tianshu Huang
    ,
    Arjun Ramesh
    ,
    Emily Ruppel
    ,
    Nuno Pereira
    ,
    Anthony Rowe
    ,
    Carlee Joe-Wong
  22. 22
    Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework
    Neel P. Bhatt
    ,
    Yunhao Yang
    ,
    Rohan Siva
    ,
    Daniel Milan
    ,
    Ufuk Topcu
    ,
    Zhangyang Wang
  23. 23
    LAVA: Lifetime-Aware VM Allocation with Learned Distributions and Adaptation to Mispredictions
    Jianheng Ling
    ,
    Pratik Worah
    ,
    Yawen Wang
    ,
    Yunchuan Kong
    ,
    Chunlei Wang
    ,
    Clifford Stein
    ,
    Diwakar Gupta
    ,
    Jason Behmer
    ,
    Logan A. Bush
    ,
    Prakash Ramanan
    ,
    Rajesh Kumar
    ,
    Thomas Chestna
    ,
    Yajing Liu
    ,
    Ying Liu
    ,
    Ye Zhao
    ,
    Kathryn S. McKinley
    ,
    Meeyoung Park
    ,
    Martin Maas
  24. 24
    LeanAttention: Hardware-Aware Scalable Attention Mechanism for the Decode-Phase of Transformers
    Rya Sanovar
    ,
    Srikant Bharadwaj
    ,
    Renée St. Amant
    ,
    Victor Rühle
    ,
    Saravan Rajmohan
  25. 25
    Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollers
    Francesco Daghero
    ,
    Daniele Jahier Pagliari
    ,
    Francesco Conti
    ,
    Luca Benini
    ,
    Massimo Poncino
    ,
    Alessio Burrello
  26. 26
    LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention
    Shang Yang
    ,
    Junxian Guo
    ,
    Haotian Tang
    ,
    Qinghao Hu
    ,
    Guangxuan Xiao
    ,
    Jiaming Tang
    ,
    Yujun Lin
    ,
    Zhijian Liu
    ,
    Yao Lu
    ,
    Song Han
  27. 27
    Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training
    Mingyu Liang
    ,
    Hiwot Tadese Kassa
    ,
    Wenyin Fu
    ,
    Brian Coutinho
    ,
    Louis Feng
    ,
    Christina Delimitrou
  28. 28
    Marconi: Prefix Caching for the Era of Hybrid LLMs
    Rui Pan
    ,
    Zhuang Wang
    ,
    Zhen Jia
    ,
    Can Karakus
    ,
    Luca Zancato
    ,
    Tri Dao
    ,
    Yida Wang
    ,
    Ravi Netravali
  29. 29
    Mas-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-constrained Edge Devices
    Mohammadali Shakerdargah
    ,
    Shan Lu
    ,
    Chao Gao
    ,
    Di Niu
  30. 30
    MEADOW: Memory-efficient Dataflow and Data Packing for Low Power Edge LLMs
    Abhishek Moitra
    ,
    Arkapravo Ghosh
    ,
    Shrey Agrawal
    ,
    Aporva Amarnath
    ,
    Karthik Swaminathan
    ,
    Priyadarshini Panda
  31. 31
    MiLo: Efficient Quantized MoE Inference with Mixture of Low-Rank Compensators
    Beichen Huang
    ,
    Yueming Yuan
    ,
    Zelei Shao
    ,
    Minjia Zhang
  32. 32
    NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference
    Xuanlin Jiang
    ,
    Yang Zhou
    ,
    Shiyi Cao
    ,
    Ion Stoica
    ,
    Minlan Yu
  33. 33
    On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions
    Maximilian Böther
    ,
    Abraham Sebastian
    ,
    Pranjal Awasthi
    ,
    Ana Klimovic
    ,
    Srikumar Ramalingam
  34. 34
    Optimizing LLM Queries in Relational Data Analytics Workloads
    Shu Liu
    ,
    Asim Biswal
    ,
    Amog Kamsetty
    ,
    Audrey Cheng
    ,
    Luis Gaspar Schroeder
    ,
    Liana Patel
    ,
    Shiyi Cao
    ,
    Xiangxi Mo
    ,
    Ion Stoica
    ,
    Joseph E. Gonzalez
    ,
    Matei Zaharia
  35. 35
    Photon: Federated LLM Pre-Training
    Lorenzo Sani
    ,
    Alex Iacob
    ,
    Zeyu Cao
    ,
    Royson Lee
    ,
    Bill Marino
    ,
    Yan Gao
    ,
    Wanru Zhao
    ,
    Dongqi Cai
    ,
    Zexi Li
    ,
    Xinchi Qiu
    ,
    Nicholas D. Lane
  36. 36
    PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training
    Daiyaan Arfeen
    ,
    Zhen Zhang
    ,
    Xinwei Fu
    ,
    Gregory R. Ganger
    ,
    Yida Wang
  37. 37
    ProtoRAIL: A Risk-cognizant Imitation Agent for Adaptive vCPU Oversubscription In the Cloud
    Lu Wang
    ,
    Mayukh Das
    ,
    Fangkai Yang
    ,
    Bo Qiao
    ,
    Hang Dong
    ,
    Si Qin
    ,
    Victor Rühle
    ,
    Chetan Bansal
    ,
    Eli Cortez
    ,
    Íñigo Goiri
    ,
    Saravan Rajmohan
    ,
    Qingwei Lin
    ,
    Dongmei Zhang
  38. 38
    QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
    Yujun Lin
    ,
    Haotian Tang
    ,
    Shang Yang
    ,
    Zhekai Zhang
    ,
    Guangxuan Xiao
    ,
    Chuang Gan
    ,
    Song Han
  39. 39
    Radius: Range-based Gradient Sparsity for Large Foundation Model Pre-training
    Mingkai Zheng
    ,
    Zhao Zhang
  40. 40
    ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation
    Zhiyu Mei
    ,
    Wei Fu
    ,
    Kaiwei Li
    ,
    Guangju Wang
    ,
    Huanchen Zhang
    ,
    Yi Wu
  41. 41
    Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving
    Wei Gao
    ,
    Xinyu Zhou
    ,
    Peng Sun
    ,
    Tianwei Zhang
    ,
    Yonggang Wen
  42. 42
    Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling
    Xinyi Zhang
    ,
    Hanyu Zhao
    ,
    Wencong Xiao
    ,
    Xianyan Jia
    ,
    Fei Xu
    ,
    Yong Li
    ,
    Wei Lin
    ,
    Fangming Liu
  43. 43
    SampleAttention: Near-Lossless Acceleration of Long Context LLM Inference with Adaptive Structured Sparse Attention
    Qianchao Zhu
    ,
    Jiangfei Duan
    ,
    Chang Chen
    ,
    Siran Liu
    ,
    Xiuhong Li
    ,
    Guanyu Feng
    ,
    Xin Lv
    ,
    Xiao Chuanfu
    ,
    Dahua Lin
    ,
    Chao Yang
  44. 44
    ScaleFusion: Scalable Inference of Spatial-Temporal Diffusion Transformers for High-Resolution Long Video Generation
    Jiacheng Yang
    ,
    Jun Wu
    ,
    Zhen Zhang
    ,
    Xinwei Fu
    ,
    Zhiying Xu
    ,
    Zhen Jia
    ,
    Yida Wang
    ,
    Gennady Pekhimenko
  45. 45
    Scaling Deep Learning Training with MPMD Pipeline Parallelism
    Anxhelo Xhebraj
    ,
    Sean Lee
    ,
    Hanfeng Chen
    ,
    Vinod Grover
  46. 46
    Seesaw: High-throughput LLM Inference via Model Re-sharding
    Qidong Su
    ,
    Wei Zhao
    ,
    Xin Li
    ,
    Muralidhar Andoorveedu
    ,
    Chenhao Jiang
    ,
    Zhanda Zhu
    ,
    Kevin Song
    ,
    Christina Giannoula
    ,
    Gennady Pekhimenko
  47. 47
    Self-Data Distillation for Recovering Quality in Pruned Large Language Models
    Vithursan Thangarasa
    ,
    Ganesh Venkatesh
    ,
    Mike Lasby
    ,
    Nish Sinnadurai
    ,
    Sean Lie
  48. 48
    SOLA: Optimizing SLO Attainment for Large Language Model Serving with State-Aware Scheduling
    Ke Hong
    ,
    Xiuhong Li
    ,
    Lufang Chen
    ,
    Qiuli Mao
    ,
    Guohao Dai
    ,
    Xuefei Ning
    ,
    Shengen Yan
    ,
    Yun Liang
    ,
    Yu Wang
  49. 49
    Spa: Scaling Graph Neural Network Training on Large graphs via Probabilistic splitting
    Sandeep Polisetty
    ,
    Juelin Liu
    ,
    Yi Fung
    ,
    Seung-Hwan Lim
    ,
    Hui Guan
    ,
    Marco Serafini
  50. 50
    SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operations
    Md. Saidul Hoque Anik
    ,
    Ariful Azad