From 01d9e7822a9b5485733f570423978601ce22e57f Mon Sep 17 00:00:00 2001 From: Lingyun Yang Date: Tue, 17 Dec 2024 11:59:12 +0000 Subject: [PATCH] GITBOOK-198: Organize the papers of SoCC '24 --- README.md | 2 +- SUMMARY.md | 1 + reading-notes/conference/README.md | 16 ++++---- reading-notes/conference/eurosys-2025.md | 14 +++++-- reading-notes/conference/socc-2024.md | 47 ++++++++++++++++++++++++ 5 files changed, 66 insertions(+), 14 deletions(-) create mode 100644 reading-notes/conference/socc-2024.md diff --git a/README.md b/README.md index 603e841..2d4bdbd 100644 --- a/README.md +++ b/README.md @@ -18,7 +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). +* 12/2024: Briefly organize the papers of [EuroSys 2025](reading-notes/conference/eurosys-2025.md) (only Spring cycle); organize the papers of [SoCC 2024](reading-notes/conference/socc-2024.md). * 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). diff --git a/SUMMARY.md b/SUMMARY.md index 48c02ab..3f053f5 100644 --- a/SUMMARY.md +++ b/SUMMARY.md @@ -43,6 +43,7 @@ * [NSDI 2025](reading-notes/conference/nsdi-2025.md) * [ASPLOS 2025](reading-notes/conference/asplos-2025.md) * [EuroSys 2025](reading-notes/conference/eurosys-2025.md) + * [SoCC 2024](reading-notes/conference/socc-2024.md) * [SOSP 2024](reading-notes/conference/sosp-2024.md) * [VLDB 2024](reading-notes/conference/vldb-2024.md) * [SIGCOMM 2024](reading-notes/conference/sigcomm-2024.md) diff --git a/reading-notes/conference/README.md b/reading-notes/conference/README.md index 3bb0dea..3ff58e9 100644 --- a/reading-notes/conference/README.md +++ b/reading-notes/conference/README.md @@ -2,21 +2,19 @@ ## 2025 -| Conference | When | Where | Remarks | -| :-----------------------------: | :----------------: | :--------------------: | ----------------------------------------------- | -| [NSDI 2025](nsdi-2025.md) | Apr 28-30, 2025 | Philadelphia, PA, USA | | -| [ASPLOS 2025](asplos-2025.md) | Mar 30-Apr 3, 2025 | Rotterdam, Netherlands | Co-located with [EuroSys 2025](eurosys-2025.md) | -| [EuroSys 2025](eurosys-2025.md) | Mar 30-Apr 3, 2025 | Rotterdam, Netherlands | Co-located with [ASPLOS 2025](asplos-2025.md) | - - +| Conference | When | Where | Remarks | +| :-----------------------------: | :----------------: | :--------------------: | -------------------------------------------------- | +| [NSDI 2025](nsdi-2025.md) | Apr 28-30, 2025 | Philadelphia, PA, USA | | +| [ASPLOS 2025](asplos-2025.md) | Mar 30-Apr 3, 2025 | Rotterdam, Netherlands | Co-located with [EuroSys 2025](eurosys-2025.md) | +| [EuroSys 2025](eurosys-2025.md) | Mar 30-Apr 3, 2025 | Rotterdam, Netherlands | WIP; co-located with [ASPLOS 2025](asplos-2025.md) | ## 2024 | Conference | When | Where | Remarks | | :-----------------------------: | :----------------: | ------------------------------------------------------ | :-------------------------------------------: | -| SoCC 2024 | Nov 22-24, 2024 | Seattle, Washington, USA | | +| [SoCC 2024](socc-2024.md) | 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 | | +| [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 | | diff --git a/reading-notes/conference/eurosys-2025.md b/reading-notes/conference/eurosys-2025.md index 777a5c5..2c27c05 100644 --- a/reading-notes/conference/eurosys-2025.md +++ b/reading-notes/conference/eurosys-2025.md @@ -8,9 +8,9 @@ Paper list: [https://2025.eurosys.org/accepted-papers.html](https://2025.eurosys ## Papers -### Large Language Model (LLM) +### Large Language Models (LLMs) -* LLM Inference +* LLM inference * Fast State Restoration in LLM Serving with HCache * THU * Stateful Large Language Model Serving with Pensieve @@ -19,7 +19,7 @@ Paper list: [https://2025.eurosys.org/accepted-papers.html](https://2025.eurosys * CUHK-Shenzhen & UChicago & Stanford * T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge * USTC & MSRA -* LLM Fine-tuning +* RLHF * HybridFlow: A Flexible and Efficient RLHF Framework * HKU & ByteDance @@ -30,7 +30,7 @@ Paper list: [https://2025.eurosys.org/accepted-papers.html](https://2025.eurosys * FlowCheck: Decoupling Checkpointing and Training of Large-Scale Models * SJTU & Alibaba Cloud -### Model Serving +### ML Inference * A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inference Clusters via Faro * UIUC & IBM Research @@ -51,3 +51,9 @@ Paper list: [https://2025.eurosys.org/accepted-papers.html](https://2025.eurosys * SJTU & Microsoft & Alibaba * Multiplexing Dynamic Deep Learning Workloads with SLO-awareness in GPU Clusters * University of Macau & SIAT, CAS + +## Acronyms + +RLHF: Reinforcement Learning from Human Feedback + +ML: Machine Learning diff --git a/reading-notes/conference/socc-2024.md b/reading-notes/conference/socc-2024.md new file mode 100644 index 0000000..0561652 --- /dev/null +++ b/reading-notes/conference/socc-2024.md @@ -0,0 +1,47 @@ +# SoCC 2024 + +## Meta Info + +Homepage: [https://acmsocc.org/2024/index.html](https://acmsocc.org/2024/index.html) + +Paper list: [https://acmsocc.org/2024/schedule.html](https://acmsocc.org/2024/schedule.html) + +## Papers + +### Large Language Models (LLMs) + +* LLM inference + * Queue Management for SLO-Oriented Large Language Model Serving \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698523)] + * UIUC & IBM Research +* LLM training + * Distributed Training of Large Language Models on AWS Trainium \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698535)] + * AWS + +### Mixture of Experts (MoEs) + +* MoE inference + * MoEsaic: Shared Mixture of Experts \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698521)] + * IBM Research + +### GPU Sharing + +* KACE: Kernel-Aware Colocation for Efficient GPU Spatial Sharing \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698555)] + * Stony Brook University + +### Serverless Computing + +* On-demand and Parallel Checkpoint/Restore for GPU Applications \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698563)] + * SJTU IPADS & Shanghai Artificial Intelligence Research Institute + * **gCROP**: **G**PU **C**heckpoint/**R**estore made **O**n-demand and **P**arallel + +### Resource Scheduler + +* Scheduler for deep learning training workloads + * Hops: Fine-grained heterogeneous sensing, efficient and fair Deep Learning cluster scheduling system \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698515)] + * Anhui University & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center + +### Distributed Training + +* Generative Adversarial Networks (GANs) + * ParaGAN: A Scalable Distributed Training Framework for Generative Adversarial Networks \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698563)] + * NUS