GITBOOK-197: Briefly organize the papers of EuroSys '25 (spring)

README.md

@@ -18,6 +18,7 @@ Specifically, I have a broad interest in systems (e.g., OSDI, SOSP, NSDI, ATC, E
 
 ## Changelogs
 
+* 12/2024: Briefly organize the papers of [EuroSys 2025](reading-notes/conference/eurosys-2025.md) (only Spring cycle).
 * 09/2024: Organize the papers of [SOSP 2024](reading-notes/conference/sosp-2024.md).
 * 08/2024: Organize the papers of [VLDB 2024](reading-notes/conference/vldb-2024.md); update the reading notes of [SIGCOMM 2024](reading-notes/conference/sigcomm-2024.md); create new paper lists of [diffusion models](paper-list/artificial-intelligence/diffusion-models.md), [language models](paper-list/artificial-intelligence/language-models.md), and [deep learning recommendation models](paper-list/artificial-intelligence/dlrm.md).
 * 07/2024: Organize the papers of [SIGCOMM 2024](reading-notes/conference/sigcomm-2024.md), [ICML 2024](reading-notes/conference/icml-2024.md), [ATC 2024](reading-notes/conference/atc-2024.md), [OSDI 2024](reading-notes/conference/osdi-2024.md), [NSDI 2024](reading-notes/conference/nsdi-2024.md), [CVPR 2024](reading-notes/conference/cvpr-2024.md), [ISCA 2024](reading-notes/conference/isca-2024.md); create a new paper list of [systems for diffusion models](paper-list/systems-for-ml/diffusion-models.md); update the paper list of [systems for LLMs](paper-list/systems-for-ml/llm.md), [systems for DLRMs](paper-list/systems-for-ml/dlrm.md), and [resource scheduler](paper-list/systems-for-ml/resource-scheduler.md).

reading-notes/conference/README.md

@@ -14,9 +14,9 @@
 
 |            Conference           |        When        | Where                                                  |                    Remarks                    |
 | :-----------------------------: | :----------------: | ------------------------------------------------------ | :-------------------------------------------: |
-|            SoCC 2024            |   Nov 22-24, 2024  | Seattle, Washington, USA                               |                  **Upcoming**                 |
-|             SC 2024             |   Nov 17-22, 2024  | Atlanta, GA, USA                                       |                  **Upcoming**                 |
-|    [SOSP 2024](sosp-2024.md)    |    Nov 4-6, 2024   | Hilton Austin, Texas, USA                              |                  **Upcoming**                 |
+|            SoCC 2024            |   Nov 22-24, 2024  | Seattle, Washington, USA                               |                                               |
+|             SC 2024             |   Nov 17-22, 2024  | Atlanta, GA, USA                                       |                                               |
+|    [SOSP 2024](sosp-2024.md)    |    Nov 4-6, 2024   | Hilton Austin, Texas, USA                              |                                               |
 |    [VLDB 2024](vldb-2024.md)    |   Aug 26-30, 2024  | Guangzhou, China                                       |                       🧐                      |
 | [SIGCOMM 2024](sigcomm-2024.md) |    Aug 4-8, 2024   | Sydney, Australia                                      |                       🧐                      |
 |    [ICML 2024](icml-2024.md)    |   Jul 21-27, 2024  | Messe Wien Exhibition Congress Center, Vienna, Austria |                                               |

reading-notes/conference/eurosys-2025.md

@@ -5,3 +5,49 @@
 Homepage: [https://2025.eurosys.org](https://2025.eurosys.org)
 
 Paper list: [https://2025.eurosys.org/accepted-papers.html](https://2025.eurosys.org/accepted-papers.html)
+
+## Papers
+
+### Large Language Model (LLM)
+
+* LLM Inference
+  * Fast State Restoration in LLM Serving with HCache
+    * THU
+  * Stateful Large Language Model Serving with Pensieve
+    * NYU
+  * CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion
+    * CUHK-Shenzhen & UChicago & Stanford
+  * T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge
+    * USTC & MSRA
+* LLM Fine-tuning
+  * HybridFlow: A Flexible and Efficient RLHF Framework
+    * HKU & ByteDance
+
+### Distributed Training
+
+* JABAS: Joint Adaptive Batching and Automatic Scaling for DNN Training on Heterogeneous GPUs
+  * UNIST & Samsung
+* FlowCheck: Decoupling Checkpointing and Training of Large-Scale Models
+  * SJTU & Alibaba Cloud
+
+### Model Serving
+
+* A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inference Clusters via Faro
+  * UIUC & IBM Research
+
+### Deep Learning Compilation
+
+* SpaceFusion: Advanced Deep Learning Operator Fusion via Space-Mapping Graph
+  * SJTU
+
+### Serverless Computing
+
+* Serverless Cold Starts and Where to Find Them
+  * Huawei
+
+### GPU Sharing
+
+* Improving GPU Sharing Performance through Adaptive Bubbleless Spatial-Temporal Sharing
+  * SJTU & Microsoft & Alibaba
+* Multiplexing Dynamic Deep Learning Workloads with SLO-awareness in GPU Clusters
+  * University of Macau & SIAT, CAS