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06/02/2023
Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training
Distributed full-graph training of Graph Neural Networks (GNNs) over lar...
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05/14/2023
A Cognitive Stimulation Dialogue System with Multi-source Knowledge Fusion for Elders with Cognitive Impairment
When communicating with elders with cognitive impairment, cognitive stim...
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02/16/2023
Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform
We present Rhino, a system for accelerating tensor programs with automat...
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02/13/2023
SWIFT: Expedited Failure Recovery for Large-scale DNN Training
As the size of deep learning models gets larger and larger, training tak...
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02/13/2023
Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment
This paper presents TAG, an automatic system to derive optimized DNN tra...
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02/07/2023
Towards Robust Inductive Graph Incremental Learning via Experience Replay
Inductive node-wise graph incremental learning is a challenging task due...
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02/01/2023
Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm
Machine learning (ML) tasks are one of the major workloads in today's ed...
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09/26/2022
Optimizing DNN Compilation for Distributed Training with Joint OP and Tensor Fusion
This paper proposes DisCo, an automatic deep learning compilation module...
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05/05/2022
dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training
Distributed training using multiple devices (e.g., GPUs) has been widely...
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04/22/2022
Efficient Pipeline Planning for Expedited Distributed DNN Training
To train modern large DNN models, pipeline parallelism has recently emer...
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02/02/2022
GADGET: Online Resource Optimization for Scheduling Ring-All-Reduce Learning Jobs
Fueled by advances in distributed deep learning (DDL), recent years have...
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12/26/2021
Large-scale Machine Learning Cluster Scheduling via Multi-agent Graph Reinforcement Learning
Efficient scheduling of distributed deep learning (DL) jobs in large GPU...
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12/16/2021
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
Graph neural networks (GNNs) have extended the success of deep neural ne...
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11/19/2021
Adversarial Deep Learning for Online Resource Allocation
Online algorithm is an important branch in algorithm design. Designing o...
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10/28/2021
OneFlow: Redesign the Distributed Deep Learning Framework from Scratch
Deep learning frameworks such as TensorFlow and PyTorch provide a produc...
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08/06/2021
Toward Efficient Online Scheduling for Distributed Machine Learning Systems
Recent years have witnessed a rapid growth of distributed machine learni...
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05/28/2021
A Sum-of-Ratios Multi-Dimensional-Knapsack Decomposition for DNN Resource Scheduling
In recent years, to sustain the resource-intensive computational needs f...
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07/02/2020
DAPPLE: A Pipelined Data Parallel Approach for Training Large Models
It is a challenging task to train large DNN models on sophisticated GPU ...
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11/16/2019
Distributed Machine Learning through Heterogeneous Edge Systems
Many emerging AI applications request distributed machine learning (ML) ...
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10/14/2019
Characterizing Deep Learning Training Workloads on Alibaba-PAI
Modern deep learning models have been exploited in various domains, incl...
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09/13/2019
DL2: A Deep Learning-driven Scheduler for Deep Learning Clusters
More and more companies have deployed machine learning (ML) clusters, wh...
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05/16/2018
NFVactor: A Resilient NFV System using the Distributed Actor Model
Resilience functionality, including failure resilience and flow migratio...
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01/03/2018
Online Job Scheduling in Distributed Machine Learning Clusters
Nowadays large-scale distributed machine learning systems have been depl...
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09/13/2017
