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09/15/2023
Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates
Oobleck enables resilient distributed training of large DNN models with ...
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05/06/2023
Memory Disaggregation: Advances and Open Challenges
Compute and memory are tightly coupled within each server in traditional...
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03/04/2023
Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training
Deep learning has experienced significant growth in recent years, result...
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02/24/2023
FLINT: A Platform for Federated Learning Integration
Cross-device federated learning (FL) has been well-studied from algorith...
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12/26/2022
Packing Privacy Budget Efficiently
Machine learning (ML) models can leak information about users, and diffe...
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10/29/2022
Auxo: Heterogeneity-Mitigating Federated Learning via Scalable Client Clustering
Federated learning (FL) is an emerging machine learning (ML) paradigm th...
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08/31/2022
Orloj: Predictably Serving Unpredictable DNNs
Existing DNN serving solutions can provide tight latency SLOs while main...
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08/12/2022
Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training
Training deep neural networks (DNNs) is becoming increasingly more resou...
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06/09/2022
Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs
The need to train DNN models on end-user devices (e.g., smartphones) is ...
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06/06/2022
TPP: Transparent Page Placement for CXL-Enabled Tiered-Memory
The increasing demand for memory in hyperscale applications has led to m...
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04/07/2022
Elastic Model Aggregation with Parameter Service
Model aggregation, the process that updates model parameters, is an impo...
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01/17/2022
Efficient DNN Training with Knowledge-Guided Layer Freezing
Training deep neural networks (DNNs) is time-consuming. While most exist...
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11/09/2021
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
The Internet of Things (IoT) is on the verge of a major paradigm shift. ...
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08/16/2021
Memtrade: A Disaggregated-Memory Marketplace for Public Clouds
We present Memtrade, the first memory disaggregation system for public c...
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07/21/2021
Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning
In this paper we propose Fed-ensemble: a simple approach that bringsmode...
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05/24/2021
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
We present FedScale, a diverse set of challenging and realistic benchmar...
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10/12/2020
Oort: Informed Participant Selection for Scalable Federated Learning
Federated Learning (FL) is an emerging direction in distributed machine ...
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12/07/2019
BoPF: Mitigating the Burstiness-Fairness Tradeoff in Multi-Resource Clusters
Simultaneously supporting latency- and throughout-sensitive workloads in...
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11/22/2019
Effectively Prefetching Remote Memory with Leap
Memory disaggregation over RDMA can improve the performance of memory-co...
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10/22/2019
Mitigating the Performance-Efficiency Tradeoff in Resilient Memory Disaggregation
Memory disaggregation has received attention in recent years as a promis...
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06/17/2019
Near Optimal Coflow Scheduling in Networks
The coflow scheduling problem has emerged as a popular abstraction in th...
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05/11/2019
RDMA Performance Isolation With Justitia
Despite its increasing popularity, most of RDMA's benefits such as ultra...
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04/17/2019
Terra: Scalable Cross-Layer GDA Optimizations
Geo-distributed analytics (GDA) frameworks transfer large datasets over ...
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02/12/2019
