> For the complete documentation index, see [llms.txt](https://paper.lingyunyang.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://paper.lingyunyang.com/reading-notes/conference/osdi-2023.md).

# OSDI 2023

## Meta Info

17th USENIX Symposium on Operating Systems Design and Implementation

Homepage: <https://www.usenix.org/conference/osdi23>

Paper List: <https://www.usenix.org/conference/osdi23/technical-sessions>

## Papers

### Distributed Serving

* [AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving](https://www.usenix.org/conference/osdi23/presentation/li-zhouhan)

### DL Compiler

* [Cocktailer: Analyzing and Optimizing Dynamic Control Flow in Deep Learning](https://www.usenix.org/conference/osdi23/presentation/zhang-chen)
* [Welder: Scheduling Deep Learning Memory Access via Tile-graph](https://www.usenix.org/conference/osdi23/presentation/shi)
* [Effectively Scheduling Computational Graphs of Deep Neural Networks toward Their Domain-Specific Accelerators](https://www.usenix.org/conference/osdi23/presentation/zhao)
* [EINNET: Optimizing Tensor Programs with Derivation-Based Transformations](https://www.usenix.org/conference/osdi23/presentation/zheng)

### Hyper-parameter Tuning

* [Hydro: Surrogate-Based Hyperparameter Tuning Service in Datacenters](https://www.usenix.org/conference/osdi23/presentation/hu)

### GNN

* [MGG: Accelerating Graph Neural Networks with Fine-Grained Intra-Kernel Communication-Computation Pipelining on Multi-GPU Platforms](https://www.usenix.org/conference/osdi23/presentation/wang-yuke)

### MoE

* [Optimizing Dynamic Neural Networks with Brainstorm](https://www.usenix.org/conference/osdi23/presentation/cui)

### DLRM

* [AdaEmbed: Adaptive Embedding for Large-Scale Recommendation Models](https://www.usenix.org/conference/osdi23/presentation/lai)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://paper.lingyunyang.com/reading-notes/conference/osdi-2023.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.