awesome-AI-system
paper and its code for AI System
https://github.com/lambda7xx/awesome-AI-system
Last synced: 4 days ago
JSON representation
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Paper-Code
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Parallellism Training
- NASPipe: High Performance and Reproducible Pipeline Parallel Supernet Training via Causal Synchronous Parallelism
- Chimera: efficiently training large-scale neural networks with bidirectional pipelines SC'21
- PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models ICML'21
- Unity: Accelerating DNN Training Through Joint Optimization of Algebraic Transformations and Parallelization OSDI'22
- PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices MLSYS'23
- Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression ASPLOS'23
- AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness NeurIPS '22
- PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models ICML'21
- DAPPLE: An Efficient Pipelined Data Parallel Approach for Large Models Training PPOPP'21
- PipeDream: Pipeline Parallelism for DNN Training SOSP'19
- awesome distributed deep learning
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Researcher
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Schedule and Resource Management
- An interference-aware scheduler for fine-grained GPU sharing Resources Eurosys'24
- ElasticFlow: An Elastic Serverless Training Platform for Distributed Deep Learning ASPLOS'23
- Multi-Resource Interleaving for Deep Learning Training SIGCOMM'22
- Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training ASPLOS'24
- KungFu: Making Training in Distributed Machine Learning Adaptive OSDI'20
- PipeSwitch: Fast Pipelined Context Switching for Deep Learning Applications OSDI'20
- An interference-aware scheduler for fine-grained GPU sharing Resources Eurosys'24
- ElasticFlow: An Elastic Serverless Training Platform for Distributed Deep Learning ASPLOS'23
- Multi-Resource Interleaving for Deep Learning Training SIGCOMM'22
- Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training ASPLOS'24
- Out-of-order backprop: an effective scheduling technique for deep learning Eurosys'22
- KungFu: Making Training in Distributed Machine Learning Adaptive OSDI'20
- PipeSwitch: Fast Pipelined Context Switching for Deep Learning Applications OSDI'20
- Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training ASPLOS'24
- Out-of-order backprop: an effective scheduling technique for deep learning Eurosys'22
- An interference-aware scheduler for fine-grained GPU sharing Resources Eurosys'24
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Serving-Inference
- AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving OSDI'23
- Optimizing Dynamic Neural Networks with Brainstorm OSDI'23
- Fast and Efficient Model Serving Using Multi-GPUs with Direct-Host-Access Eurosys'23
- Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs.ASPLOS'23
- MPCFormer: fast, performant, and private transformer inference with MPC ICLR'23
- High-throughput Generative Inference of Large Language Modelwith a Single GPU ICML'23
- VELTAIR: Towards High-Performance Multi-Tenant Deep Learning Serving via Adaptive Compilation and Scheduling ASPLOS'22
- DVABatch: Diversity-aware Multi-Entry Multi-Exit Batching for Efficient Processing of DNN Services on GPUs ATC'22
- Cocktail: A Multidimensional Optimization for Model Serving in Cloud NSDI'22
- Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing ATC'22
- INFaaS: Automated Model-less Inference Serving ATC'21
- Enable Simultaneous DNN Services Based on Deterministic Operator Overlap and Precise Latency Prediction SC'21
- Serving DNNs like Clockwork: Performance Predictability from the Bottom Up OSDI'20
- Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving ATC'19
- Nexus: a GPU cluster engine for accelerating DNN-based video analysis SOSP'19
- Clipper:A low-latency prediction-serving system NSDI'17
- Enable Simultaneous DNN Services Based on Deterministic Operator Overlap and Precise Latency Prediction SC'21
- Enable Simultaneous DNN Services Based on Deterministic Operator Overlap and Precise Latency Prediction SC'21
- Enable Simultaneous DNN Services Based on Deterministic Operator Overlap and Precise Latency Prediction SC'21
- Paella: Low-latency Model Serving with Virtualized GPU Scheduling SOSP'23
- AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving OSDI'23
- Optimizing Dynamic Neural Networks with Brainstorm OSDI'23
- Fast and Efficient Model Serving Using Multi-GPUs with Direct-Host-Access Eurosys'23
- MPCFormer: fast, performant, and private transformer inference with MPC ICLR'23
- High-throughput Generative Inference of Large Language Modelwith a Single GPU ICML'23
- High-throughput Generative Inference of Large Language Modelwith a Single GPU ICML'23
- VELTAIR: Towards High-Performance Multi-Tenant Deep Learning Serving via Adaptive Compilation and Scheduling ASPLOS'22
- DVABatch: Diversity-aware Multi-Entry Multi-Exit Batching for Efficient Processing of DNN Services on GPUs ATC'22
- Cocktail: A Multidimensional Optimization for Model Serving in Cloud NSDI'22
- Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing ATC'22
- RIBBON: cost-effective and qos-aware deep learning model inference using a diverse pool of cloud computing instances SC'21
- INFaaS: Automated Model-less Inference Serving ATC'21
- Enable Simultaneous DNN Services Based on Deterministic Operator Overlap and Precise Latency Prediction SC'21
- Serving DNNs like Clockwork: Performance Predictability from the Bottom Up OSDI'20
- Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving ATC'19
- Nexus: a GPU cluster engine for accelerating DNN-based video analysis SOSP'19
- Clipper:A low-latency prediction-serving system NSDI'17
- Enable Simultaneous DNN Services Based on Deterministic Operator Overlap and Precise Latency Prediction SC'21
- Enable Simultaneous DNN Services Based on Deterministic Operator Overlap and Precise Latency Prediction SC'21
- Paella: Low-latency Model Serving with Virtualized GPU Scheduling SOSP'23
- Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs.ASPLOS'23
- MPCFormer: fast, performant, and private transformer inference with MPC ICLR'23
- VELTAIR: Towards High-Performance Multi-Tenant Deep Learning Serving via Adaptive Compilation and Scheduling ASPLOS'22
- RIBBON: cost-effective and qos-aware deep learning model inference using a diverse pool of cloud computing instances SC'21
- Fast and Efficient Model Serving Using Multi-GPUs with Direct-Host-Access Eurosys'23
- INFaaS: Automated Model-less Inference Serving ATC'21
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Training
- ModelKeeper: Accelerating DNN Training via Automated Training Warmup NSDI'23
- Whale: Efficient Giant Model Training over Heterogeneous {GPUs}ATC'22
- GeePS: Scalable Deep Learning on Distributed GPUs with a GPU-Specialized Parameter Server Eurosys'16
- ModelKeeper: Accelerating DNN Training via Automated Training Warmup NSDI'23
- STRONGHOLD: Fast and Affordable Billion-scale Deep Learning Model Training SC'22
- Whale: Efficient Giant Model Training over Heterogeneous {GPUs}ATC'22
- GeePS: Scalable Deep Learning on Distributed GPUs with a GPU-Specialized Parameter Server Eurosys'16
- STRONGHOLD: Fast and Affordable Billion-scale Deep Learning Model Training SC'22
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Categories
Sub Categories
Parallellism Training
61
Serving-Inference
46
Optimization
37
GNN
26
GPU Cluster Management
17
Schedule and Resource Management
16
MoE
14
Framework
11
Communication
10
Training
8
Misc
6
LLM Serving
5
LLM Serving Framework
5
Fancy LLM
5
Energy
4
Fine-Tune
3
LLM FineTune
2
LoRA
1
LLM Inference (System Side)
1
LLM Evaluation Platform
1
LLM Robustness and Debugging
1
Researcher
1
Attention
1