Vincent Lannurien
d822cdaa6a
4.4 KiB
title, venue, year, date, tags, paper_count, draft
| title | venue | year | date | tags | paper_count | draft |
|---|---|---|---|---|---|---|
| OSDI 2025 Digest | OSDI | 2025 | 2025-01-01 | 13 | false |
13 papers selected.
Basilisk: Using Provenance Invariants to Automate Proofs of Undecidable Protocols
Tony Nuda Zhang, Keshav Singh, Tej Chajed, Manos Kapritsos et al.
TL;DR — Automates the construction of correctness proofs for distributed protocols that were previously considered undecidable, advancing the state of the art in verified systems.
Mako: Speculative Distributed Transactions with Geo-Replication
Weihai Shen, Yang Cui, Siddhartha Sen 0001, Sebastian Angel et al.
TL;DR — Combines speculative execution with geo-replication to deliver low-latency distributed transactions without sacrificing consistency, addressing a fundamental tension in wide-area systems.
Low End-to-End Latency atop a Speculative Shared Log with Fix-Ante Ordering
Shreesha G. Bhat, Tony Hong, Xuhao Luo, Jiyu Hu et al.
TL;DR — Introduces fix-ante ordering to achieve low latency on a shared log without sacrificing throughput, offering a new design point for log-based distributed storage.
Okapi: Decoupling Data Striping and Redundancy Grouping in Cluster File Systems
Sanjith Athlur, Timothy Kim, Saurabh Kadekodi, Francisco Maturana et al.
TL;DR — Challenges a long-standing coupling in erasure-coded cluster file systems, enabling independent optimization of striping and redundancy with measurable gains in production workloads.
PoWER Never Corrupts: Tool-Agnostic Verification of Crash Consistency and Corruption Detection
Hayley LeBlanc, Jacob R. Lorch, Chris Hawblitzel, Cheng Huang et al.
TL;DR — Provides a tool-agnostic framework for formally verifying crash consistency and corruption detection in storage systems, raising the bar for storage software correctness.
EMT: An OS Framework for New Memory Translation Architectures
Siyuan Chai 0001, Jiyuan Zhang 0003, Jongyul Kim 0001, Alan Wang et al.
TL;DR — Defines an OS abstraction layer that decouples applications from hardware-specific memory translation mechanisms, enabling future memory architectures to be adopted without OS rewrites.
XSched: Preemptive Scheduling for Diverse XPUs
Weihang Shen, Mingcong Han, Jialong Liu, Rong Chen 0001 et al.
TL;DR — Generalises preemptive scheduling to heterogeneous accelerators (XPUs), providing a unified OS-level mechanism for fair and responsive multi-tenant accelerator sharing.
Extending Applications Safely and Efficiently
Yusheng Zheng, Tong Yu, Yiwei Yang 0002, Yanpeng Hu et al.
TL;DR — Presents a principled model for safe, efficient application extensibility that generalises beyond eBPF, with implications for the design of future OS extension mechanisms.
NanoFlow: Towards Optimal Large Language Model Serving Throughput
Kan Zhu, Yufei Gao, Yilong Zhao 0002, Liangyu Zhao et al.
TL;DR — Analytically characterises the throughput ceiling for LLM serving and proposes a system that approaches that bound through fine-grained intra-device parallelism.
WaferLLM: Large Language Model Inference at Wafer Scale
Congjie He, Yeqi Huang, Pei Mu 0003, Ziming Miao et al.
TL;DR — Demonstrates end-to-end LLM inference on wafer-scale hardware, tackling novel challenges in memory, communication, and fault tolerance at an unprecedented scale of integration.
Mirage: A Multi-Level Superoptimizer for Tensor Programs
Mengdi Wu, Xinhao Cheng, Shengyu Liu, Chunan Shi et al.
TL;DR — Extends tensor program superoptimisation to multiple abstraction levels, discovering non-obvious kernel fusions that outperform hand-tuned implementations for ML workloads.
Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks
Yuxuan Jiang 0016, Ziming Zhou, Boyu Xu 0005, Beijie Liu et al.
TL;DR — Addresses the underappreciated problem of silent hardware and software errors in large-scale DL training, providing automated proactive checks that catch failures before they corrupt long training runs.
Compass: Encrypted Semantic Search with High Accuracy
Jinhao Zhu, Liana Patel, Matei Zaharia, Raluca Ada Popa
TL;DR — Enables accurate semantic (vector) search over encrypted data, bridging the gap between privacy-preserving computation and modern retrieval workloads in cloud-hosted RAG systems.