OSDI '24 Poster Session

Published Work

Posters of papers published at OSDI '24.

ACCL+: an FPGA-Based Collective Engine for Distributed Applications
Zhenhao He, Dario Korolija, Yu Zhu, and Benjamin Ramhorst, Systems Group, ETH Zurich; Tristan Laan, University of Amsterdam; Lucian Petrica and Michaela Blott, AMD Research; Gustavo Alonso, Systems Group, ETH Zurich

Anvil: Verifying Liveness of Cluster Management Controllers
Xudong Sun, Wenjie Ma, Jiawei Tyler Gu, and Zicheng Ma, University of Illinois Urbana-Champaign; Tej Chajed, University of Wisconsin-Madison; Jon Howell, Andrea Lattuada, and Oded Padon, VMware Research; Lalith Suresh, Feldera; Adriana Szekeres, VMware Research; Tianyin Xu, University of Illinois Urbana-Champaign

Automatically Reasoning About How Systems Code Uses the CPU Cache
Rishabh Iyer, Katerina Argyraki, and George Candea, EPFL

Beaver: Practical Partial Snapshots for Distributed Cloud Services
Liangcheng Yu, University of Pennsylvania; Xiao Zhang, Shanghai Jiao Tong University; Haoran Zhang, University of Pennsylvania; John Sonchack, Princeton University; Dan Ports, Microsoft / University of Washington; Vincent Liu, University of Pennsylvania

Caravan: Practical Online Learning of In-Network ML Models with Labeling Agents
Qizheng Zhang, Stanford University; Ali Imran, Purdue University; Enkeleda Bardhi, Sapienza University of Rome; Tushar Swamy and Nathan Zhang, Stanford University; Muhammad Shahbaz, Purdue University and University of Michigan; Kunle Olukotun, Stanford University

ChameleonAPI: Automatic and Efficient Customization of Neural Networks for ML Applications
Yuhan Liu, University of Chicago; Chengcheng Wan, East China Normal University; Kuntai Du, Henry Hoffmann, and Junchen Jiang, University of Chicago; Shan Lu, University of Chicago and Microsoft Research; Michael Maire, University of Chicago

Data-flow Availability: Achieving Timing Assurance in Autonomous Systems
Ao Li and Ning Zhang, Washington University in St. Louis

DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving
Yinmin Zhong and Shengyu Liu, Peking University; Junda Chen, UC San Diego; Jianbo Hu, Peking University; Yibo Zhu, StepFun; Xuanzhe Liu and Xin Jin, Peking University; Hao Zhang, UC San Diego

DSig: Breaking the Barrier of Signatures in Data Centers
Marcos K. Aguilera, VMware Research Group; Clément Burgelin, Rachid Guerraoui, and Antoine Murat, École Polytechnique Fédérale de Lausanne (EPFL); Athanasios Xygkis, Oracle Labs; Igor Zablotchi, Mysten Labs

Enabling Tensor Language Model to Assist in Generating High-Performance Tensor Programs for Deep Learning
Yi Zhai, University of Science and Technology of China; Sijia Yang, Huawei Technologies Co., Ltd.; Keyu Pan, ByteDance Ltd.; Renwei Zhang, Huawei Technologies Co., Ltd.; Shuo Liu, University of Science and Technology of China; Chao Liu and Zichun Ye, Huawei Technologies Co., Ltd.; Jianmin Ji, University of Science and Technology of China; Jie Zhao, Hunan University; Yu Zhang and Yanyong Zhang, University of Science and Technology of China

Fairness in Serving Large Language Models
Ying Sheng, UC Berkeley and Stanford University; Shiyi Cao, Dacheng Li, Banghua Zhu, and Zhuohan Li, UC Berkeley; Danyang Zhuo, Duke University; Joseph E. Gonzalez and Ion Stoica, UC Berkeley

FairyWREN: A Sustainable Cache for Emerging Write-Read-Erase Flash Interfaces
Sara McAllister and Yucong "Sherry" Wang, Carnegie Mellon University; Benjamin Berg, UNC Chapel Hill; Daniel S. Berger, Microsoft Azure and University of Washington; George Amvrosiadis, Nathan Beckmann, and Gregory R. Ganger, Carnegie Mellon University

Flock: A Framework for Deploying On-Demand Distributed Trust
Darya Kaviani and Sijun Tan, UC Berkeley; Pravein Govindan Kannan, IBM Research; Raluca Ada Popa, UC Berkeley

High-throughput and Flexible Host Networking for Accelerated Computing
Athinagoras Skiadopoulos, Zhiqiang Xie, and Mark Zhao, Stanford University; Qizhe Cai and Saksham Agarwal, Cornell University; Jacob Adelmann, David Ahern, Carlo Contavalli, Michael Goldflam, Vitaly Mayatskikh, Raghu Raja, and Daniel Walton, Enfabrica; Rachit Agarwal, Cornell University; Shrijeet Mukherjee, Enfabrica; Christos Kozyrakis, Stanford University

Identifying On-/Off-CPU Bottlenecks Together with Blocked Samples
Minwoo Ahn and Jeongmin Han, Sungkyunkwan University; Youngjin Kwon, Korea Advanced Institute of Science and Technology (KAIST); Jinkyu Jeong, Yonsei University

Inductive Invariants That Spark Joy: Using Invariant Taxonomies to Streamline Distributed Protocol Proofs
Tony Nuda Zhang, University of Michigan; Travis Hance, Carnegie Mellon University; Manos Kapritsos, University of Michigan; Tej Chajed, University of Wisconsin–Madison; Bryan Parno, Carnegie Mellon University

InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management
Wonbeom Lee, Jungi Lee, Junghwan Seo, and Jaewoong Sim, Seoul National University

IronSpec: Increasing the Reliability of Formal Specifications
Eli Goldweber, Weixin Yu, Seyed Armin Vakil Ghahani, and Manos Kapritsos, University of Michigan

Llumnix: Dynamic Scheduling for Large Language Model Serving
Biao Sun, Ziming Huang, Hanyu Zhao, Wencong Xiao, Xinyi Zhang, Yong Li, and Wei Lin, Alibaba Group

Managing Memory Tiers with CXL in Virtualized Environments
Yuhong Zhong, Columbia University, Microsoft Azure; Daniel S. Berger, Microsoft Azure, University of Washington; Carl Waldspurger, Carl Waldspurger Consulting; Ryan Wee, Columbia University; Ishwar Agarwal, Rajat Agarwal, Frank Hady, and Karthik Kumar, Intel; Mark D. Hill, University of Wisconsin–Madison; Mosharaf Chowdhury, University of Michigan; Asaf Cidon, Columbia University

Microkernel Goes General: Performance and Compatibility in the HongMeng Production Microkernel
Haibo Chen, Huawei Central Software Institute and Shanghai Jiao Tong University; Xie Miao, Ning Jia, Nan Wang, Yu Li, Nian Liu, Yutao Liu, Fei Wang, Qiang Huang, Kun Li, Hongyang Yang, Hui Wang, Jie Yin, Yu Peng, and Fengwei Xu, Huawei Central Software Institute

Nomad: Non-Exclusive Memory Tiering via Transactional Page Migration
Lingfeng Xiang, Zhen Lin, Weishu Deng, Hui Lu, and Jia Rao, The University of Texas at Arlington; Yifan Yuan and Ren Wang, Intel Labs

Optimizing Resource Allocation in Hyperscale Datacenters: Scalability, Usability, and Experiences
Neeraj Kumar, Pol Mauri Ruiz, Vijay Menon, Igor Kabiljo, Mayank Pundir, Andrew Newell, Daniel Lee, Liyuan Wang, and Chunqiang Tang, Meta Platforms

Parrot: Efficient Serving of LLM-based Applications with Semantic Variable
Chaofan Lin, Shanghai Jiao Tong University; Zhenhua Han, Chengruidong Zhang, Yuqing Yang, and Fan Yang, Microsoft Research; Chen Chen, Shanghai Jiao Tong University; Lili Qiu, Microsoft Research

Performance Interfaces for Hardware Accelerators
Jiacheng Ma, Rishabh Iyer, Sahand Kashani, Mahyar Emami, Thomas Bourgeat, and George Candea, EPFL

Secret Key Recovery in a Global-Scale End-to-End Encryption System
Graeme Connell, Signal Messenger; Vivian Fang, UC Berkeley; Rolfe Schmidt, Signal Messenger; Emma Dauterman and Raluca Ada Popa, UC Berkeley

ServerlessLLM: Low-Latency Serverless Inference for Large Language Models
Yao Fu, Leyang Xue, Yeqi Huang, and Andrei-Octavian Brabete, University of Edinburgh; Dmitrii Ustiugov, NTU Singapore; Yuvraj Patel and Luo Mai, University of Edinburgh

ServiceLab: Preventing Tiny Performance Regressions at Hyperscale through Pre-Production Testing
Mike Chow, Meta Platforms; Yang Wang, Meta Platforms and The Ohio State University; William Wang, Ayichew Hailu, Rohan Bopardikar, Bin Zhang, Jialiang Qu, David Meisner, Santosh Sonawane, Yunqi Zhang, Rodrigo Paim, Mack Ward, Ivor Huang, Matt McNally, Daniel Hodges, Zoltan Farkas, Caner Gocmen, Elvis Huang, and Chunqiang Tang, Meta Platforms

Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve
Amey Agrawal, Georgia Institute of Technology; Nitin Kedia, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, and Bhargav Gulavani, Microsoft Research India; Alexey Tumanov, Georgia Institute of Technology; Ramachandran Ramjee, Microsoft Research India

When will my ML Job finish? Toward providing Completion Time Estimates through Predictability-Centric Scheduling
Abdullah Bin Faisal, Noah Martin, Hafiz Mohsin Bashir, Swaminathan Lamelas, and Fahad R. Dogar, Tufts University

μSlope: High Compression and Fast Search on Semi-Structured Logs
Rui Wang, YScope; Devin Gibson, YScope and University of Toronto; Kirk Rodrigues, YScope; Yu Luo, YScope, Uber, and University of Toronto; Yun Zhang, Kaibo Wang, Yupeng Fu, and Ting Chen, Uber; Ding Yuan, YScope and University of Toronto