# Memory Disaggregation

## CXL-based Disaggregation

* CXL-ANNS: Software-Hardware Collaborative Memory Disaggregation and Computation for Billion-Scale Approximate Nearest Neighbor Search ([ATC 2023](/reading-notes/conference/atc-2023.md)) \[[Paper](https://www.usenix.org/conference/atc23/presentation/jang)]
  * KAIST & Panmnesia, Inc.
  * Approximate nearest neighbor search (ANNS) services.
* Overcoming the Memory Wall with CXL-Enabled SSDs ([ATC 2023](/reading-notes/conference/atc-2023.md)) \[[Paper](https://www.usenix.org/conference/atc23/presentation/yang-shao-peng)]
  * Syracuse & DGIST & FADU Inc. & Soongsil
  * CXL-flash: CXL-enabled *flash device*; caching and prefetching.
* TPP: Transparent Page Placement for CXL-Enabled Tiered-Memory ([ASPLOS 2023](/reading-notes/conference/asplos-2023.md)) \[[Personal Notes](/reading-notes/conference/asplos-2023/tpp.md)] \[[Paper](https://dl.acm.org/doi/10.1145/3582016.3582063)] \[[Code](https://lwn.net/Articles/876993/)]
  * UMich SymbioticLab & NVIDIA & Meta
  * CXL 1.1
  * Identify and place hot/cold pages to appropriate memory tiers (i.e., local memory or CXL memory).
* Pond: CXL-Based Memory Pooling Systems for Cloud Platforms ([ASPLOS 2023](/reading-notes/conference/asplos-2023.md)) \[[Paper](https://dl.acm.org/doi/abs/10.1145/3575693.3578835)]
  * Microsoft Azure
* Direct Access, High-Performance Memory Disaggregation with DirectCXL ([ATC 2022](/reading-notes/conference/atc-2022.md)) \[[Personal Notes](/reading-notes/conference/atc-2022/directcxl.md)] \[[Paper](https://www.usenix.org/conference/atc22/presentation/gouk)]
  * KAIST
  * CXL 2.0
  * 6.2x shorter latency & 3x better performance than RDMA-based memory disaggregation.

## RDMA-based Disaggregation

* DisaggRec: Architecting Disaggregated Systems for Large-Scale Personalized Recommendation (arXiv 2212.00939) \[[Personal Notes](broken://pages/PZK9r9RRQdKqiSGt6JW4)] \[[Paper](https://arxiv.org/abs/2212.00939)]
  * Meta AI & WashU & UPenn & Cornell & Intel
  * Deep learning recommendation models; partition *embedding tables*.
* Hydra: Resilient and Highly Available Remote Memory (FAST 2022) \[[Paper](https://www.usenix.org/conference/fast22/presentation/lee)] \[[Code](https://github.com/SymbioticLab/Hydra)]
  * UMich SymbioticLab
  * In-memory erasure coding.
* Rethinking Software Runtimes for Disaggregated Memory (ASPLOS 2021) \[[Paper](https://dl.acm.org/doi/10.1145/3445814.3446713)] \[[Code](https://github.com/project-kona/asplos21-ae)]
  * VMWare & Penn State & ETH & EPFL & UMich & Google
  * Kona
  * Cache coherence.
* Effectively Prefetching Remote Memory with Leap ([ATC 2020](/reading-notes/conference/atc-2020.md)) \[[Paper](https://www.usenix.org/conference/atc20/presentation/al-maruf)] \[[Code](https://github.com/SymbioticLab/leap)]
  * UMich SymbioticLab
  * **Best Paper**
  * Memory prefetching
* LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation ([OSDI 2018](/reading-notes/conference/osdi-2018.md)) \[[Paper](https://www.usenix.org/conference/osdi18/presentation/shan)] \[[Code](https://github.com/WukLab/LegoOS)]
  * Purdue
  * **Best Paper**
  * *Splitkernel*
* Efficient Memory Disaggregation with Infiniswap (NSDI 2017) \[[Paper](https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/gu)] \[[Code](https://github.com/SymbioticLab/Infiniswap)]
  * UMich SymbioticLab
  * Remote memory paging system.


---

# Agent Instructions: 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/paper-list/resource-disaggregation/memory-disaggregation.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.