Paper-Code

• Parallellism Training

  • DAPPLE: An Efficient Pipelined Data Parallel Approach for Large Models Training PPOPP'21

• Researcher

  • ![Website

• 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

• 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

• 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