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site/content/cloud-edge/digests/OSDI-2025/index.md

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+---
+title: OSDI 2025 Digest
+venue: OSDI
+year: 2025
+date: '2025-01-01'
+tags: []
+paper_count: 13
+draft: 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.
+