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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Curated collection of papers in machine learning systems
| Date | Stars |
|---|---|
| 2026-08-02 | 639 |
| 2026-08-06 | 639 |
| 2026-08-07 | 640 |
| 2026-08-12 | 641 |
| 2026-08-14 | 642 |
| 2026-08-15 | 641 |
| 2026-08-18 | 643 |
| 2026-08-20 | 644 |
| 2026-08-24 | 645 |
| 2026-08-26 | 646 |
| 2026-08-27 | 648 |
| 2026-08-28 | 649 |
| 2026-09-01 | 650 |
| 2026-09-03 | 650 |
| 2026-09-09 | 651 |
| 2026-09-10 | 652 |
| 2026-09-11 | 653 |
| 2026-09-13 | 654 |
| 2026-09-16 | 655 |
| 2026-09-23 | 656 |
| 2026-09-26 | 655 |
| 2026-09-28 | 656 |
| 2026-10-07 | 656 |
| 2026-10-08 | 656 |
| 2026-10-17 | 657 |
| 2026-10-19 | 658 |
| 2026-10-20 | 657 |
| 2026-10-28 | 656 |
| 2026-10-29 | 658 |
| 2026-10-31 | 659 |
| 2026-11-02 | 660 |
| 2026-11-03 | 661 |
| 2026-11-04 | 661 |
Today
— stars today
This week
+5 stars this week
This month
+5 stars this month
Momentum
5.0
growth rate 0.76%/day
# Paper List for Machine Learning Systems
[](https://github.com/byungsoo-oh/ml-systems-papers/pulls)
A curated list of machine learning systems papers published in major CS conferences (plus some workshops and journals).
> Survey papers are annotated with `[Survey 🔍]`.
## arXiv Papers
For arXiv preprints, please see [`README_arxiv.md`](README_arxiv.md).
## Table of Contents
<!-- TOC -->
- [Paper List for Machine Learning Systems](#paper-list-for-machine-learning-systems)
- [arXiv Papers](#arxiv-papers)
- [Table of Contents](#table-of-contents)
- [Data Processing](#data-processing)
- [Training System](#training-system)
- [ML job analysis on GPU clusters](#ml-job-analysis-on-gpu-clusters)
- [Resource scheduling](#resource-scheduling)
- [Distributed training](#distributed-training)
- [RL post-training](#rl-post-training)
- [AutoML](#automl)
- [GNN training](#gnn-training)
- [Inference System](#inference-system)
- [Attention Optimization](#attention-optimization)
- [Mixture of Experts (MoE)](#mixture-of-experts-moe)
- [Communication Optimization \& Network Infrastructure for Distributed ML](#communication-optimization--network-infrastructure-for-distributed-ml)
- [Fault tolerance \& Straggler mitigation](#fault-tolerance--straggler-mitigation)
- [GPU Memory Management \& Optimization](#gpu-memory-management--optimization)
- [GPU Sharing](#gpu-sharing)
- [Compiler](#compiler)
- [GPU Kernel Optimization](#gpu-kernel-optimization)
- [LLM Long Context](#llm-long-context)
- [Model Compression](#model-compression)
- [Federated Learning](#federated-learning)
- [Privacy-Preserving ML](#privacy-preserving-ml)
- [ML APIs \& Application-Side Optimization](#ml-apis--application-side-optimization)
- [ML for Systems](#ml-for-systems)
- [Energy Efficiency](#energy-efficiency)
- [Retrieval-Augmented Generation (RAG)](#retrieval-augmented-generation-rag)
- [Simulation](#simulation)
- [Systems for Agentic AI](#systems-for-agentic-ai)
- [Multimodal](#multimodal)
- [Hybrid LLMs](#hybrid-llms)
- [Others](#others)
<!-- /TOC -->
## Data Processing
**Data pipeline optimization**
- [VLDB'25] [cedar: Composable and Optimized Machine Learning Input Data Pipelines](https://arxiv.org/abs/2401.08895)
- [HotInfra'24] [Lotus: Characterize Architecture Level CPU-based Preprocessing in Machine Learning Pipelines](https://kexinrong.github.io/lab/files/lotus-hotinfra24.pdf)
- [MLSys'22] Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
- [ISCA'22] Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training
- [SIGMOD'22] Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines
- [VLDB'21] Analyzing and Mitigating Data Stalls in DNN Training
- [VLDB'21] tf.data: A Machine Learning Data Processing Framework
**Preprocessing stalls**
- [FAST'26] [Preparation Meets Opportunity: Enhancing Data Preprocessing for ML Training With Seneca](https://www.usenix.org/conference/fast26/presentation/desai)
- [ATC'24] [Pecan: Cost-Efficient ML Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement](https://www.usenix.org/conference/atc24/presentation/graur)
- [HotStorage'24] [A Selective Preprocessing Offloading Framework for Reducing Data Traffic in DL Training](https://dl.acm.org/doi/abs/10.1145/3655038.3665947)
- [VLDB'24] [FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation](https://www.vldb.org/pvldb/vol17/p863-kim.pdf)
- [CVPR'23] [FFCV: Accelerating Training by Removing Data Bottlenecks](https://openaccess.thecvf.com/content/CVPR2023/papers/Leclerc_FFCV_Accelerating_Training_by_Removing_Data_Bottlenecks_CVPR_2023_paper.pdf)
- [RecSys'23] [InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep ReExcerpt of 151,072 characters
Read on GitHub
42
Jintao Zhang · @thu-ml, Tsinghua University · China
4
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d82ebad219c038bb, topic:awesome-list