Paper List for Machine Learning Systems

Paper list for broad topics in machine learning systems

NOTE: Survey papers are annotated with [Survey 🔍] prefix.

Table of Contents

• Paper List for Machine Learning Systems
  • 1. Data Processing
    • 1.1 Data pipeline optimization
      • 1.1.1 General
      • 1.1.2 Prep stalls
      • 1.1.3 Fetch stalls (I/O)
      • 1.1.4 Specific workloads (GNN, DLRM)
    • 1.2 Caching and Distributed storage for ML training
    • 1.3 Data formats
    • 1.4 Data pipeline fairness and correctness
    • 1.5 Data labeling automation
  • 2. Training System
    • 2.1 Empirical Study on ML Jobs
    • 2.2 DL scheduling
    • 2.3 GPU sharing
    • 2.4 GPU memory management and optimization
    • 2.5 GPU memory usage estimate
    • 2.6 Distributed training (Parallelism)
      • 2024
      • 2023
      • 2022
      • 2021
      • 2020
      • ~2019
      • Survey Papers
    • 2.6 DL job failures
    • 2.7 Model checkpointing
    • 2.8 AutoML
    • 2.9 Communication optimization
    • 2.10 Energy-efficient DNN training (carbon-aware)
    • 2.11 DNN compiler
    • 2.12 Model pruning and compression
    • 2.13 GNN training system
    • 2.14 Congestion control for DNN training
  • 3. Inference System
  • 4. Federated Learning
  • 5. Privacy-Preserving ML
  • 6. ML APIs & Application-side Optimization
  • 7. ML for Systems
  • Others
• References

1. Data Processing

1.1 Data pipeline optimization

1.1.1 General

• [arxiv'24] cedar: Composable and Optimized Machine Learning Input Data Pipelines
• [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

1.1.2 Prep stalls

• [VLDB'24] FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation
• [arxiv'23] Rinas: Training with Dataset Shuffling Can Be General and Fast
• [CVPR'23] FFCV: Accelerating Training by Removing Data Bottlenecks
• [RecSys'23] InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models
• [SIGMOD'23] GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning
• [VLDB'23] FastFlow: Accelerating Deep Learning Model Training with Smart Offloading of Input Data Pipeline
• [SoCC'23] tf.data service: A Case for Disaggregating ML Input Data Processing
  • arxiv version
• [ATC'22] Cachew: Machine Learning Input Data Processing as a Service
• [OSDI'22] Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters
• [ICPP'19] DLBooster: Boosting End-to-End Deep Learning Workflows with Offloading Data Preprocessing Pipelines

1.1.3 Fetch stalls (I/O)

• [TACO'23] Fastensor: Optimise the Tensor I/O Path from SSD to GPU for Deep Learning Training
• [ICPP'22] Lobster: Load Balance-Aware I/O for Distributed DNN Training
• [SC'21] Clairvoyant Prefetching for Distributed Machine Learning I/O

1.1.4 Specific workloads (GNN, DLRM)

• [arxiv'23] Towards Data-centric Graph Machine Learning: Review and Outlook
• [arxiv'23] FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models
• [MLSys'23] RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
• [ASPLOS'22] RecShard: statistical feature-based memory optimization for industry-scale neural recommendation
• [RecSys'23] InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models
• [arxiv'23] MTrainS: Improving DLRM training efficiency using heterogeneous memories
• [SOSP'23] Bagpipe: Accelerating Deep Recommendation Model Training
• [SOSP'23] gSampler: General and Efficient GPU-based Graph Sampling for Graph Learning
• [NSDI'23] BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
• [DAC'22] A Joint Management Middleware to Improve Training Performance of Deep Recommendation Systems with SSDs
• [VLDB'22] Accelerating Recommendation System Training by Leveraging Popular Choices

1.2 Caching and Distributed storage for ML training

• [TPDS'23] High-Level Data Abstraction and Elastic Data Caching for Data-Intensive AI Applications on Cloud-Native Platforms
• [SOSP'23] UGACHE: A Unified GPU Cache for Embedding-based Deep Learning
• [ATC'23] Tectonic-Shift: A Composite Storage Fabric for Large-Scale ML Training
• [EuroSys'23] SiloD: A Co-design of Caching and Scheduling for Deep Learning Clusters [also in 2.1]
• [FAST'23] SHADE: Enable Fundamental Cacheability for Distributed Deep Learning Training
• [HPCA'23] iCACHE: An Importance-Sampling-Informed Cache for Accelerating I/O-Bound DNN Model Training
• [NeurIPS'22] A Deep Learning Dataloader with Shared Data Preparation
• [CLUSTER'22] Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications
• [ICDE'22] Fluid: Dataset Abstraction and Elastic Acceleration for Cloud-native Deep Learning Training Jobs
• [ATC'21] Refurbish Your Training Data: Reusing Partially Augmented Samples for Faster Deep Neural Network Training
• [FAST'20] Quiver: An Informed Storage Cache for Deep Learning
• [ICPP'20] DIESEL: A Dataset-Based Distributed Storage and Caching System for Large-Scale Deep Learning Training
• [arXiv'19] Faster Neural Network Training with Data Echoing
• [HotCloud'19] The Case for Unifying Data Loading in Machine Learning Clusters

1.3 Data formats

• [ECCV'22] L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training
• [VLDB'21] Progressive compressed records: Taking a byte out of deep learning data

1.4 Data pipeline fairness and correctness

• [CIDR'21] Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines

1.5 Data labeling automation

• [VLDB'18] Snorkel: Rapid Training Data Creation with Weak Supervision

2. Training System

2.1 Empirical Study on ML Jobs

• [ICSE'24] An Empirical Study on Low GPU Utilization of Deep Learning Jobs
• [NSDI'22] MLaaS in the wild: workload analysis and scheduling in large-scale heterogeneous GPU clusters (PAI)
• [ATC'19] Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads (Philly)

2.2 DL scheduling

• [Middleware'24] Optimal Resource Efficiency with Fairness in Heterogeneous GPU Clusters

• [IPDPS'24] Hadar: Heterogeneity-Aware Optimization-Based Online Scheduling for Deep Learning Cluster

• [EuroSys'24] Blox: A Modular Toolkit for Deep Learning Schedulers

• [NSDI'24] Swing: Short-cutting Rings for Higher Bandwidth Allreduce

• [NSDI'24] Towards Domain-Specific Network Transport for Distributed DNN Training

• [NSDI'24] Vulcan: Automatic Query Planning for Live ML Analytics

• [NSDI'24] CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters

• [Survey :mag:] [ACM CSUR'23] Deep Learning Workload Scheduling in GPU Datacenters: A Survey

• [arxiv'23] Energy-Efficient GPU Clusters Scheduling for Deep Learning

• [SC'23] EasyScale: Accuracy-consistent Elastic Training for Deep Learning

• [ICPP'23] CoTrain: Efficient Scheduling for Large-Model Training upon GPU and CPU in Parallel

• [ICPP'23] Embracing Uncertainty for Equity in Resource Allocation in ML Training

• [SOSP'23] Sia: Heterogeneity-aware, goodput-optimized ML-cluster scheduling

• [NSDI'23] Shockwave: Proactive, Fair, and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning

• [EuroSys'23] SiloD: A Co-design of Caching and Scheduling for Deep Learning Clusters [also in 1.2]

• [EuroSys'23] Lyra: Elastic Scheduling for Deep Learning Clusters

• [EuroSys'23] ElasticFlow: An Elastic Serverless Training Platform for Distributed Deep Learning

• [ASPLOS'23] Lucid: A Non-intrusive, Scalable and Interpretable Scheduler for Deep Learning Training Jobs

• [arxiv'22] Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads

• [Survey :mag:] [arxiv, 2022] Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision

• [SoCC'22] ESCHER: Expressive Scheduling with Ephemeral Resources

• [NSDI'22] MLaaS in the wild: workload analysis and scheduling in large-scale heterogeneous GPU clusters (PAI)

• [OSDI'22] Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters (Synergy)

• [SIGCOMM'22] Multi-resource interleaving for deep learning training (Muri)

• [MLSys'21] Wavelet: Efficient DNN Training with Tick-Tock Scheduling

• [SoCC'21] Chronus: A Novel Deadline-aware Scheduler for Deep Learning Training Jobs

• [SC'21] Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters (Helios)

• [OSDI'21] Privacy Budget Scheduling (DPF)

• [NSDI'21] Elastic Resource Sharing for Distributed Deep Learning (AFS)

• [OSDI'21] Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning

• [EuroSys'20] Balancing efficiency and fairness in heterogeneous GPU clusters for deep learning (GandivaFair)

• [NSDI'20] Themis: Fair and Efficient GPU Cluster Scheduling

• [OSDI'20] HiveD: Sharing a GPU Cluster for Deep Learning with Guarantees

• [OSDI'20] Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads (Gavel)

• [EuroSys'20] AlloX: Compute Allocation in Hybrid Clusters

• [MLSys'20] Resource Elasticity in Distributed Deep Learning

• [NSDI'19] Tiresias: A GPU Cluster Manager for Distributed Deep Learning

• [ATC'19] Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads (Philly)

• [EuroSys'18] Optimus: an efficient dynamic resource scheduler for deep learning clusters

• [OSDI'18] Gandiva: Introspective Cluster Scheduling for Deep Learning

2.3 GPU sharing

• [EuroSys'24 (to appear)] Orion: Interference-aware, Fine-grained GPU Sharing for ML Applications
• [ATC'23] Beware of Fragmentation: Scheduling GPU-Sharing Workloads with Fragmentation Gradient Descent
• [NSDI'23] Transparent GPU Sharing in Container Clouds for Deep Learning Workloads
• [ICPP'23] FaST-GShare: Enabling Efficient Spatio-Temporal GPU Sharing in Serverless Computing for Deep Learning Inference
• [arxiv'23] MuxFlow: Efficient and Safe GPU Sharing in Large-Scale Production Deep Learning Clusters
• [SoCC'22] MISO: exploiting multi-instance GPU capability on multi-tenant GPU clusters
• [PACT'22] GPUPool: A Holistic Approach to Fine-Grained GPU Sharing in the Cloud
• [ATC'21] Zico: Efficient GPU Memory Sharing for Concurrent DNN Training
• [MLSys'20] Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications
• [OSDI'20] AntMan: Dynamic Scaling on GPU Clusters for Deep Learning
• [OSDI'20] PipeSwitch: Fast Pipelined Context Switching for Deep Learning Applications

2.4 GPU memory management and optimization

• [arxiv'24] GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
• [ASPLOS'24] GMLake: Efficient and Transparent GPU Memory Defragmentation for Large-scale DNN Training with Virtual Memory Stitching
• [arxiv'23] Rethinking Memory and Communication Cost for Efficient Large Language Model Training
• [arxiv'23] Quantized Distributed Training of Large Models with Convergence Guarantees (QSDP)
• [arxiv'23] Does compressing activations help model parallel training?
• [SoCC'23] Towards GPU Memory Efficiency for Distributed Training at Scale
• [VLDB'23] PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
• [SOSP'23] Efficient Memory Management for Large Language Model Serving with PagedAttention
• [HPCA'23] MPress: Democratizing Billion-Scale Model Training on Multi-GPU Servers via Memory-Saving Inter-Operator Parallelism
• [HPCA'23] Tensor Movement Orchestration in Multi-GPU Training Systems
• [IJCAI'23] OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning
• [ICLR'22] LoRA: Low-Rank Adaptation of Large Language Models
  • algorithmic method for memory efficiency
• [VLDB'22] Harmony: Overcoming the Hurdles of GPU Memory Capacity to Train Massive DNN Models on Commodity Servers
• [ATC'21] ZeRO-Offload: Democratizing Billion-Scale Model Training
• [ICLR'21] ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
• [ICLR'21] Dynamic Tensor Rematerialization
• [SC'21] ZeRO-infinity: breaking the GPU memory wall for extreme scale deep learning
• [HPCA'21] Sentinel: Efficient Tensor Migration and Allocation on Heterogeneous Memory Systems for Deep Learning
• [MLSys'20] Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
• [ASPLOS'20] Capuchin: Tensor-based GPU Memory Management for Deep Learning
• [ASPLOS'20] SwapAdvisor: Pushing Deep Learning Beyond the GPU Memory Limit via Smart Swapping
• [SC'20] ZeRO: memory optimizations toward training trillion parameter models
• [ISCA'18] Gist: Efficient Data Encoding for Deep Neural Network Training
• [PPoPP'18] Superneurons: dynamic GPU memory management for training deep neural networks
• [MICRO'16] vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design
• [arxiv'16] Training Deep Nets with Sublinear Memory Cost

2.5 GPU memory usage estimate

• [ESEC/FSE'20] Estimating GPU memory consumption of deep learning models

2.6 Distributed training (Parallelism)

2024

• [arxiv'24] Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
• [arxiv'24] Accelerating Heterogeneous Tensor Parallelism via Flexible Workload Control
• [arxiv'24] GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models
• [ICLR'24] Zero Bubble (Almost) Pipeline Parallelism
• [arxiv'24] BitDelta: Your Fine-Tune May Only Be Worth One Bit
• [arxiv'24] NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language Models
• [arxiv'24] Accelerating Parallel Sampling of Diffusion Models
• [arxiv'24] Training DNN Models over Heterogeneous Clusters with Optimal Performance
• [NSDI'24] DISTMM: Accelerating Distributed Multi-modal Model Training
• [NSDI'24] Accelerating Neural Recommendation Training with Embedding Scheduling
• [NSDI'24] Resiliency at Scale: Managing Google’s TPUv4 Machine Learning Supercomputer
• [NSDI'24] QuickUpdate: a Real-Time Personalization System for Large-Scale Recommendation Models
• [NSDI'24] Scaling Large Language Model Training to More Than 10,000 GPUs
• [arxiv'24] Breaking MLPerf Training: A Case Study on Optimizing BERT
• [ICLR'24] CO2: Efficient Distributed Training with Full Communication-Computation Overlap
  • arxiv openreview
• [arxiv'24] LocMoE: A Low-overhead MoE for Large Language Model Training
• [arxiv'24] Re-evaluating the Memory-balanced Pipeline Parallelism: BPipe
• [AAMAS'24] Holonic Learning: A Flexible Agent-based Distributed Machine Learning Framework
• [arxiv'24] InternEvo: Efficient Long-sequence Large Language Model Training via Hybrid Parallelism and Redundant Sharding
• [VLDB'24] Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads
• [HPCA'24] Tessel: Boosting Distributed Execution of Large DNN Models via Flexible Schedule Search
• [NSDI'24] Parcae: Proactive, Liveput-Optimized DNN Training on Preemptible Instances
• [EuroSys'24] HAP: SPMD DNN Training on Heterogeneous GPU Clusters with Automated Program Synthesis

2023

• [arxiv'23] LightSeq: Sequence Level Parallelism for Distributed Training of Long Context Transformers

• [ICPP'23] Mercury: Fast and Optimal Device Placement for Large Deep Learning Models

• [arxiv'23] TENPLEX: Changing Resources of Deep Learning Jobs using Parallelizable Tensor Collections

• [arxiv'23] vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training

• [arxiv'23] ASPEN: High-Throughput LoRA Fine-Tuning of Large Language Models with a Single GPU

• [arxiv'23] FlexModel: A Framework for Interpretability of Distributed Large Language Models

• [arxiv'23] Holmes: Towards Distributed Training Across Clusters with Heterogeneous NIC Environment

• [arxiv'23] RTP: Rethinking Tensor Parallelism with Memory Deduplication

• [arxiv'23] FP8-LM: Training FP8 Large Language Models

• [arxiv'23] Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs

• [arxiv'23] DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

• [arxiv'23] A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale

• [arxiv'23] FLM-101B: An Open LLM and How to Train It with $100K Budget

• [arxiv'23] UniAP: Unifying Inter- and Intra-Layer Automatic Parallelism by Mixed Integer Quadratic Programming

• [arxiv'23] Improving Automatic Parallel Training via Balanced Memory Workload Optimization

  • extended version of Galvatron (VLDB'23)

• [arxiv'23] Modeling Parallel Programs using Large Language Models

• [arxiv'23] Proteus: Simulating the Performance of Distributed DNN Training

• [arxiv'23] Automated Tensor Model Parallelism with Overlapped Communication for Efficient Foundation Model Training

• [arxiv'23] Decoupled Model Schedule for Deep Learning Training

• [arxiv'23] RAF: Holistic Compilation for Deep Learning Model Training

• [arxiv'23] Ada-Grouper: Accelerating Pipeline Parallelism in Preempted Network by Adaptive Group-Scheduling for Micro-Batches

• [arxiv'23] Does compressing activations help model parallel training?

• [arxiv'23] Colossal-Auto: Unified Automation of Parallelization and Activation Checkpoint for Large-scale Models

• [arxiv'23] Scaling Vision Transformers to 22 Billion Parameters

• [arxiv'23] Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform

• [arxiv'23] TAP: Accelerating Large-Scale DNN Training Through Tensor Automatic Parallelisation

• [arxiv'23] SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction

• [arxiv'23] ATP: Adaptive Tensor Parallelism for Foundation Models

• [arxiv'23] AutoDDL: Automatic Distributed Deep Learning with Asymptotically Optimal Communication

• [IPDPS'23] MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism

• [CLUSTER'23] Prophet: Fine-grained Load Balancing for Parallel Training of Large-scale MoE Models

• [NeurIPS'23] DeepPCR: Parallelizing Sequential Operations in Neural Networks

• [DAC'23] MixPipe: Efficient Bidirectional Pipeline Parallelism for Training Large-Scale Models

• [SC'23] Hanayo: Harnessing Wave-like Pipeline Parallelism for Enhanced Large Model Training Efficiency

• [SOSP'23] PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation

• [SOSP'23] Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates

• [TPDS'23] Fold3D: Rethinking and Parallelizing Computational and Communicational Tasks in the Training of Large DNN Models

• [HPCA'23] Phloem: Automatic Acceleration of Irregular Applications with Fine-Grain Pipeline Parallelism

• [ACL'23] Sequence Parallelism: Long Sequence Training from System Perspective

• [CCGrid'23] A Deep Learning Pipeline Parallel Optimization Method

• [OSDI'23] MGG: Accelerating Graph Neural Networks with Fine-Grained Intra-Kernel Communication-Computation Pipelining on Multi-GPU Platforms

• [ATC'23] Accelerating Distributed MoE Training and Inference with Lina

• [ATC'23] SmartMoE: Efficiently Training Sparsely-Activated Models through Combining Offline and Online Parallelization

• [ATC'23] MSRL: Distributed Reinforcement Learning with Dataflow Fragments

• [Survey :mag:] [TPDS'23] A Survey on Auto-Parallelism of Large-Scale Deep Learning Training

• [ICML'23] SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient

• [ICML'23] BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models

• [ICS'23] A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training

• [NSDI'23] TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs

• [NSDI'23] Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs

• [NSDI'23] ARK: GPU-driven Code Execution for Distributed Deep Learning

• [SIGMOD'23] FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement

• [MLSys'23] On Optimizing the Communication of Model Parallelism

• [MLSys'23] Tutel: Adaptive Mixture-of-Experts at Scale

• [TPDS'23] Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models

• [PPoPP'23] Elastic Averaging for Efficient Pipelined DNN Training

• [PPoPP'23] Efficient All-Reduce for Distributed DNN Training in Optical Interconnect Systems

• [VLDB'23] MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

• [VLDB'23] Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

• [ASPLOS'23] Mobius: Fine Tuning Large-Scale Models on Commodity GPU Servers

• [ASPLOS'23] Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression

2022

• [arxiv'22] MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
• [arxiv'22] Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training
• [arxiv'22] Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
• [ICPP'22] Tesseract: Parallelize the Tensor Parallelism Efficiently
• [MLSys'22] Synthesizing optimal parallelism placement and reduction strategies on hierarchical systems for deep learning
  • arxiv
• [NeurIPS'22] Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees
• [SoCC'22] Accelerating Large-Scale Distributed Neural Network Training with SPMD Parallelism
• [MLSys'22] Pathways: Asynchronous distributed dataflow for ML
• [MLSys'22] SRIFTY: Swift and Thrifty Distributed Neural Network Training on the Cloud
• [MLSys'22] Efficient Strong Scaling Through Burst Parallel Training
• [EuroSys'22] Varuna: scalable, low-cost training of massive deep learning models
• [ATC'22] Whale: Efficient Giant Model Training over Heterogeneous GPUs
• [NeurIPS'22] AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness
• [PPoPP'22] FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models
• [ICML'22] DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
• [HPDC'22] Hare: Exploiting Inter-job and Intra-job Parallelism of Distributed Machine Learning on Heterogeneous GPUs
• [OSDI'22] Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning
• [NSDI'22] Accelerating Collective Communication in Data Parallel Training across Deep Learning Frameworks

2021

• [arxiv'21] Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training
• [arxiv'21] GSPMD: General and Scalable Parallelization for ML Computation Graphs
• [JMLR'21] Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
• [TPDS'21] TensorOpt: Exploring the Tradeoffs in Distributed DNN Training With Auto-Parallelism
• [ATC'21] Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelism
• [SIGMOD'21] Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce [also in 2.10]
• [MLSys'21] PipeMare: Asynchronous Pipeline Parallel DNN Training
• [ICLR'21] GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
• [NeurIPS'21] Piper: Multidimensional Planner for DNN Parallelization
• [ICML'21] Memory-Efficient Pipeline-Parallel DNN Training
• [ICML'21] TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
• [ICML'21] PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models
• [SC'21] Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines
• [SC'21] Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM (PTD-P or Megatron-LM v2)
• [FAST'21] Behemoth: A Flash-centric Training Accelerator for Extreme-scale DNNs
• [PPoPP'21] DAPPLE: a pipelined data parallel approach for training large models
• [VLDB'21] Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches

2020

• [HPCA'20] AccPar: Tensor Partitioning for Heterogeneous Deep Learning Accelerators
• [NeurIPS'20] Efficient Algorithms for Device Placement of DNN Graph Operators
• [arxiv'20] Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
• [KDD'20 Tutorial] DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters
• [VLDB'20] PyTorch Distributed: Experiences on Accelerating Data Parallel Training
• [OSDI'20] A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU/CPU Clusters (BytePS)
• [SOSP'19] PipeDream: Generalized Pipeline Parallelism for DNN Training
• [NeurIPS'20] Language Models are Few-Shot Learners [From OpenAI]
  • arxiv
• [arxiv'20] Scaling Laws for Neural Language Models [From OpenAI]

~2019

• [HPCA'19] HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
• [IEEE MICRO'19] Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training
• [MLSys'19] Beyond data and model parallelism for deep neural networks (FlexFlow)
• [MLSys'19] TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
• [EuroSys'19] Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks
• [EuroSys'19] Supporting Very Large Models using Automatic Dataflow Graph Partitioning (Tofu)
• [SOSP'19] A Generic Communication Scheduler for Distributed DNN Training Acceleration
• [NeurIPS'19] Mesh-TensorFlow: Deep Learning for Supercomputers
• [NeurIPS'19] GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
• [ICML'18] Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks

Survey Papers

• [Survey :mag:] [IJCAI'22] Survey on Effcient Training of Large Neural Networks
• [Survey :mag:] [ACM CSUR'19] Demystifying Parallel and Distributed Deep Learning
• [Survey :mag:] [ACM CSUR'19] Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques, and Tools

2.6 DL job failures

• [ATC'22] Sibylla: To Retry or Not To Retry on Deep Learning Job Failure
• [ICSE'20] An Empirical Study on Program Failures of Deep Learning Jobs

2.7 Model checkpointing

• [FAST'21] CheckFreq: Frequent, Fine-Grained DNN Checkpointing

2.8 AutoML

• [OSDI'23] Hydro: Surrogate-Based Hyperparameter Tuning Service in Datacenters
• [NSDI'23] ModelKeeper: Accelerating DNN Training via Automated Training Warmup
• [OSDI'20] Retiarii: A Deep Learning Exploratory-Training Framework

2.9 Communication optimization

• [arxiv'24] MLTCP: Congestion Control for DNN Training
• [arxiv'24] Accelerating Distributed Deep Learning using Lossless Homomorphic Compression
• [NSDI'24] THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
• [arxiv'23] Optimized Network Architectures for Large Language Model Training with Billions of Parameters
• [arxiv'23] FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models
• [arxiv'23] Rethinking Memory and Communication Cost for Efficient Large Language Model Training
• [arxiv'23] Zen: Near-Optimal Sparse Tensor Synchronization for Distributed DNN Training
• [arxiv'23] Optimized Network Architectures for Large Language Model Training with Billions of Parameters
• [arxiv'23] ZeRO++: Extremely Efficient Collective Communication for Giant Model Training
• [arxiv'23] TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Training
• [ICML'23] CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks
  • Related to DT-FM (NeurIPS'22)
• [IPDPS'23] MCR-DL: Mix-and-Match Communication Runtime for Deep Learning
• [ASPLOS'23] MSCCLang: Microsoft Collective Communication Language
• [ASPLOS'23] Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models
• [EuroSys'23] A2TP: Aggregator-aware In-network Aggregation for Multi-tenant Learning
• [EuroSys'23] Hi-Speed DNN Training with Espresso: Unleashing the Full Potential of Gradient Compression with Near-Optimal Usage Strategies
• [MLSys'23] Cupcake: A Compression Optimizer for Scalable Communication-Efficient Distributed Training
• [MLSys'23] On Optimizing the Communication of Model Parallelism
• [NSDI'23] Better Together: Jointly Optimizing ML Collective Scheduling and Execution Planning using SYNDICATE
• [NSDI'23] TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches
• [ISCA'22] Themis: a network bandwidth-aware collective scheduling policy for distributed training of DL models
• [SC'22] HammingMesh: A Network Topology for Large-Scale Deep Learning
• [PPoPP'22] Near-optimal sparse allreduce for distributed deep learning
• [MLSys'22] Synthesizing optimal parallelism placement and reduction strategies on hierarchical systems for deep learning (P^2)
  • arxiv
• [SIGMOD'21] Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce [also in 2.5]
• [SC'21] Flare: flexible in-network allreduce
• [NSDI'21] Scaling Distributed Machine Learning with In-Network Aggregation
• [ISCA'21] Enabling compute-communication overlap in distributed deep learning training platforms
• [PPoPP'21] Synthesizing optimal collective algorithms (SCCL)
• [PPoPP'20] Taming unbalanced training workloads in deep learning with partial collective operations
• [MLSys'20] Blink: Fast and Generic Collectives for Distributed ML
• [MLSys'20] PLink: Discovering and Exploiting Datacenter Network Locality for Efficient Cloud-based Distributed Training
• [MLSys'19] Priority-based Parameter Propagation for Distributed DNN Training (P3)
• [MLSys'19] TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
• [SOSP'19] A generic communication scheduler for distributed DNN training acceleration (ByteScheduler)
• [ATC'17] Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters

2.10 Energy-efficient DNN training (carbon-aware)

• [arxiv'23] Perseus: Removing Energy Bloat from Large Model Training
• [arxiv'23] CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
• [ATC'23] EnvPipe: Performance-preserving DNN Training Framework for Saving Energy
• [NSDI'23] Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

2.11 DNN compiler

• [OSDI'23] Cocktailer: Analyzing and Optimizing Dynamic Control Flow in Deep Learning
• [OSDI'23] Welder: Scheduling Deep Learning Memory Access via Tile-graph
• [OSDI'23] Effectively Scheduling Computational Graphs of Deep Neural Networks toward Their Domain-Specific Accelerators
• [OSDI'23] EINNET: Optimizing Tensor Programs with Derivation-Based Transformations
• [OSDI'22] ROLLER: Fast and Efficient Tensor Compilation for Deep Learning
• [OSDI'20] Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks
• [OSDI'20] Ansor: Generating High-Performance Tensor Programs for Deep Learning
• [ASPLOS'20] FlexTensor: An Automatic Schedule Exploration and Optimization Framework for Tensor Computation on Heterogeneous System
• [OSDI'18] TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

2.12 Model pruning and compression

• [ACL'23] Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
• [ICLR'23] GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
• [OSDI'23] AdaEmbed: Adaptive Embedding for Large-Scale Recommendation Models
• [ICML'22] TSPipe: Learn from Teacher Faster with Pipelines

2.13 GNN training system

For comprehensive list of GNN systems papers, refer to https://github.com/chwan1016/awesome-gnn-systems.

• [VLDB'24] NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams
• [arxiv'23] ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training
• [arxiv'23] Helios: An Efficient Out-of-core GNN Training System on Terabyte-scale Graphs with In-memory Performance
• [arxiv'23] GNNPipe: Accelerating Distributed Full-Graph GNN Training with Pipelined Model Parallelism
• [MLSys'23] Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training
• [SIGMOD'23] DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU
• [OSDI'23] MGG: Accelerating Graph Neural Networks with Fine-Grained Intra-Kernel Communication-Computation Pipelining on Multi-GPU Platforms
• [EuroSys'23] MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks
• [KDD'22] Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Heterogeneous Graphs
• [VLDB'22] TGL: a general framework for temporal GNN training on billion-scale graphs
• [OSDI'21] P3: Distributed Deep Graph Learning at Scale

2.14 Congestion control for DNN training

• [arxiv'24] MLTCP: Congestion Control for DNN Training
• [HotNets'22] Congestion Control in Machine Learning Clusters

3. Inference System

• [arxiv'24] Minions: Accelerating Large Language Model Inference with Adaptive and Collective Speculative Decoding
• [arxiv'24] ALTO: An Efficient Network Orchestrator for Compound AI Systems
• [ASPLOS'24] NeuPIMs: NPU-PIM Heterogeneous Acceleration for Batched LLM Inferencing
• [MLSys'24] HeteGen: Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices
• [arxiv'24] ATP: Enabling Fast LLM Serving via Attention on Top Principal Keys
• [arxiv'24] Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve
• [arxiv'24] DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving
• [ICLR'24] Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs
• [arxiv'24] FlexLLM: A System for Co-Serving Large Language Model Inference and Parameter-Efficient Finetuning
• [arxiv'24] Wisdom of Committee: Distilling from Foundation Model to SpecializedApplication Model
• [arxiv'24] RelayAttention for Efficient Large Language Model Serving with Long System Prompts
• [arxiv'24] LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization
  • [PPoPP'24 poster] POSTER: LLM-PQ:Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization
• [NSDI'24] Approximate Caching for Efficiently Serving Diffusion Models
• [NSDI'24] Characterization of Large Language Model Development in the Datacenter
• [arxiv'24] APIServe: Efficient API Support for Large-Language Model Inferencing
• [arxiv'24] ServerlessLLM: Locality-Enhanced Serverless Inference for Large Language Models
• [arxiv'24] MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving
• [arxiv'24] FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design
• [arxiv'24] Accelerating Retrieval-Augmented Language Model Serving with Speculation
• [arxiv'24] CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference
• [arxiv'24] Inference without Interference: Disaggregate LLM Inference for Mixed Downstream Workloads
• [arxiv'24] DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving
• [arxiv'24] DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference
• [Survey :mag:] [arxiv'24] Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding
• [arxiv'24] Learned Best-Effort LLM Serving
• [arxiv'24] Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache
• [VLDB'24] Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity
• [ASPLOS'24] SpotServe: Serving Generative Large Language Models on Preemptible Instances
• [arxiv'23] Splitwise: Efficient generative LLM inference using phase splitting
• [arxiv'23] SpecInfer: Accelerating Generative Large Language Model Serving with Speculative Inference and Token Tree Verification
• [arxiv'23] Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding
• [arxiv'23] Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving
• [arxiv'23] Fairness in Serving Large Language Models
• [arxiv'23] HexGen: Generative Inference of Foundation Model over Heterogeneous Decentralized Environment
• [arxiv'23] Moirai: Towards Optimal Placement for Distributed Inference on Heterogeneous Devices
• [arxiv'23] Punica: Multi-Tenant LoRA Serving
• [arxiv'23] Pipeline Parallelism for DNN Inference with Practical Performance Guarantees
• [arxiv'23] SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills
• [arxiv'23] High-throughput Generative Inference of Large Language Models with a Single GPU
• [HPDC'23] Kairos: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources
• [SOSP'23] Paella: Low-latency Model Serving with Virtualized GPU Scheduling
• [SOSP'23] Efficient Memory Management for Large Language Model Serving with PagedAttention
• [MLSys'23] Efficiently Scaling Transformer Inference
• [EuroSys'23] Fast and Efficient Model Serving Using Multi-GPUs with Direct-Host-Access
• [EuroSys'23] Tabi: An Efficient Multi-Level Inference System for Large Language Models
• [EuroSys'23] Pocket: ML Serving from the Edge
• [OSDI'23] AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving
• [NSDI'23] SHEPHERD: Serving DNNs in the Wild
• [VLDB'23] Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures
• [ICML'23] Fast Inference from Transformers via Speculative Decoding
• [SIGMOD'22] Serverless Data Science - Are We There Yet? A Case Study of Model Serving
• [OSDI'22] Orca: A Distributed Serving System for Transformer-Based Generative Models
• [OSDI'22] Microsecond-scale Preemption for Concurrent GPU-accelerated DNN Inferences
• [ATC'22] SOTER: Guarding Black-box Inference for General Neural Networks at the Edge
• [ATC'22] Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing
• [ATC'22] Tetris: Memory-efficient Serverless Inference through Tensor Sharing
• [ATC'22] PetS: A Unified Framework for Parameter-Efficient Transformers Serving
• [ATC'21] INFaaS: Automated Model-less Inference Serving
• [SoCC'21] Morphling: Fast, Near-Optimal Auto-Configuration for Cloud-Native Model Serving
• [arxiv'21] Supporting Massive DLRM Inference through Software Defined Memory
• [MobiCom'20] SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud

4. Federated Learning

• [arxiv'24] Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data
• [SAC'24] Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
• [arxiv'23] CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
• [arxiv'23] Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
• [IMWUT'23] AttFL: A Personalized Federated Learning Framework for Time-series Mobile and Embedded Sensor Data Processing
• [Survey :mag:] [FGCS'23] Model aggregation techniques in federated learning: A comprehensive survey
• [SoCC'23] Auxo: Heterogeneity-Mitigating Federated Learning via Scalable Client Clustering
• [MLSys'23] GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
• [WWW'23] To Store or Not? Online Data Selection for Federated Learning with Limited Storage
• [EuroSys'23] REFL: Resource-Efficient Federated Learning
• [VLDB'23] FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
• [RecSys'22] Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
• [TMLR'22] Optimal Client Sampling for Federated Learning
• [ICML'22] FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
• [MobiSys'22] FedBalancer: data and pace control for efficient federated learning on heterogeneous clients
• [MobiCom'22] PyramidFL: A Fine-grained Client Selection Framework for Efficient Federated Learning
• [MLSys'22] PAPAYA: Practical, Private, and Scalable Federated Learning
• [AISTATS'22] Federated Learning with Buffered Asynchronous Aggregation
• [NeurIPS'21] Federated Reconstruction: Partially Local Federated Learning
• [NeurIPS'21] FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
• [OSDI'21] Oort: Efficient Federated Learning via Guided Participant Selection
• [MICRO'21] AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
• [MLSys'19] Towards Federated Learning at Scale: System Design
• [Survey :mag:] [ACM CSUR'22] Federated Learning for Smart Healthcare: A Survey

5. Privacy-Preserving ML

• [DAC'23] Privacy-Preserving DNN Training with Prefetched Meta-Keys on Heterogeneous Neural Network Accelerators
• [ICLR'23] MPCFormer: fast, performant and private Transformer inference with MPC
• [NeurIPS'22] Iron: Private Inference on Transformers

6. ML APIs & Application-side Optimization

• [arxiv'24] APIServe: Efficient API Support for Large-Language Model Inferencing
• [OSDI'24 (to appear)] Automatic and Efficient Customization of Neural Networks for ML Applications
• [ICML'22] Efficient Online ML API Selection for Multi-Label Classification Tasks (FrugalMCT)
• [NeurIPS'20] FrugalML: How to use ML Prediction APIs more accurately and cheaply

7. ML for Systems

• [arxiv'24] Large Language Model Adaptation for Networking
• [arxiv'24] LLM-Enhanced Data Management
• [arxiv'24] MPIrigen: MPI Code Generation through Domain-Specific Language Models
• [arxiv'24] Can Large Language Models Write Parallel Code?
• [arxiv'23] LLM-Assisted Code Cleaning For Training Accurate Code Generators
• [arxiv'23] Large Language Models for Compiler Optimization
• [VLDB'23] How Large Language Models Will Disrupt Data Management

Others

• [arxiv'24] You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation
• [arxiv'24] Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
• [Survey :mag:] [arxiv'24] A Survey of Resource-efficient LLM and Multimodal Foundation Models
• [arxiv'23] Efficiently Programming Large Language Models using SGLang
• [MICRO'23] Path Forward Beyond Simulators: Fast and Accurate GPU Execution Time Prediction for DNN Workloads

References

This repository is motivated by:

• https://github.com/HuaizhengZhang/Awesome-System-for-Machine-Learning
• https://github.com/S-Lab-System-Group/Awesome-DL-Scheduling-Papers
• https://github.com/ganler/ResearchReading
• https://jeongseob.github.io/readings_mlsys.html
• https://github.com/chwan1016/awesome-gnn-systems
• https://github.com/ConnollyLeon/awesome-Auto-Parallelism