240 citations · 571 across the 28 of their papers we have counts for
28 papers
OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination
Jae-Won Chung, Zhirui Liang, Yanyong Mao +3
AI's growing compute demand and new datacenter buildouts present major capacity and reliability challenges for the electricity grid, leading to multi-year interconnection delays fo…
Venn: Resource Management for Collaborative Learning Jobs
Jiachen Liu, Fan Lai, Ding Ding +2
In recent years, collaborative learning (CL) has emerged as a promising approach for machine learning (ML) and data science across distributed edge devices. As the deployment of CL…
Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates
Insu Jang, Zhenning Yang, Zhen Zhang +2
Oobleck enables resilient distributed training of large DNN models with guaranteed fault tolerance. It takes a planning-execution co-design approach, where it first generates a set…
Memory Disaggregation: Advances and Open Challenges
Hasan Al Maruf, Mosharaf Chowdhury
Compute and memory are tightly coupled within each server in traditional datacenters. Large-scale datacenter operators have identified this coupling as a root cause behind fleet-wi…
Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training
Zhenning Yang, Luoxi Meng, Jae-Won Chung +1
Deep learning has experienced significant growth in recent years, resulting in increased energy consumption and carbon emission from the use of GPUs for training deep neural networ…
FLINT: A Platform for Federated Learning Integration
Ewen Wang, Ajay Kannan, Yuefeng Liang +2
Cross-device federated learning (FL) has been well-studied from algorithmic, system scalability, and training speed perspectives. Nonetheless, moving from centralized training to c…
DPack: Efficiency-Oriented Privacy Budget Scheduling
Pierre Tholoniat, Kelly Kostopoulou, Mosharaf Chowdhury +4
Machine learning (ML) models can leak information about users, and differential privacy (DP) provides a rigorous way to bound that leakage under a given budget. This DP budget can…
Auxo: Efficient Federated Learning via Scalable Client Clustering
Jiachen Liu, Fan Lai, Yinwei Dai +3
Federated learning (FL) is an emerging machine learning (ML) paradigm that enables heterogeneous edge devices to collaboratively train ML models without revealing their raw data to…
Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training
Jie You, Jae-Won Chung, Mosharaf Chowdhury
Training deep neural networks (DNNs) is becoming increasingly more resource- and energy-intensive every year. Unfortunately, existing works primarily focus on optimizing DNN traini…
Orloj: Predictably Serving Unpredictable DNNs
Peifeng Yu, Yuqing Qiu, Xin Jin +1
Existing DNN serving solutions can provide tight latency SLOs while maintaining high throughput via careful scheduling of incoming requests, whose execution times are assumed to be…
Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs
Sanjay Sri Vallabh Singapuram, Fan Lai, Chuheng Hu +1
The need to train DNN models on end-user devices (e.g., smartphones) is increasing with the need to improve data privacy and reduce communication overheads. Unlike datacenter serve…
TPP: Transparent Page Placement for CXL-Enabled Tiered-Memory
Hasan Al Maruf, Hao Wang, Abhishek Dhanotia +7
The increasing demand for memory in hyperscale applications has led to memory becoming a large portion of the overall datacenter spend. The emergence of coherent interfaces like CX…
Elastic Model Aggregation with Parameter Service
Juncheng Gu, Mosharaf Chowdhury, Kang G. Shin +1
Model aggregation, the process that updates model parameters, is an important step for model convergence in distributed deep learning (DDL). However, the parameter server (PS), a p…
Treehouse: A Case For Carbon-Aware Datacenter Software
Thomas Anderson, Adam Belay, Mosharaf Chowdhury +2
The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path. Datacenters are already a significant fraction of worldwid…
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing
Yiding Wang, Decang Sun, Kai Chen +2
Training deep neural networks (DNNs) is time-consuming. While most existing solutions try to overlap/schedule computation and communication for efficient training, this paper goes…
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
Raed Kontar, Naichen Shi, Xubo Yue +12
The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is br…
Memtrade: A Disaggregated-Memory Marketplace for Public Clouds
Hasan Al Maruf, Yuhong Zhong, Hongyi Wang +3
We present Memtrade, the first memory disaggregation system for public clouds. Public clouds introduce a set of unique challenges for resource disaggregation across different tenan…
Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning
Naichen Shi, Fan Lai, Raed Al Kontar +1
In this paper we propose Fed-ensemble: a simple approach that bringsmodel ensembling to federated learning (FL). Instead of aggregating localmodels to update a single global model,…
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Fan Lai, Yinwei Dai, Sanjay S. Singapuram +4
We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research. FedScale datasets encompass a w…
Oort: Efficient Federated Learning via Guided Participant Selection
Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha +1
Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. Despite having the same end goal…
BoPF: Mitigating the Burstiness-Fairness Tradeoff in Multi-Resource Clusters
Tan N. Le, Xiao Sun, Mosharaf Chowdhury +1
Simultaneously supporting latency- and throughout-sensitive workloads in a shared environment is an increasingly more common challenge in big data clusters. Despite many advances,…
Effectively Prefetching Remote Memory with Leap
Hasan Al Maruf, Mosharaf Chowdhury
Memory disaggregation over RDMA can improve the performance of memory-constrained applications by replacing disk swapping with remote memory accesses. However, state-of-the-art mem…
Hydra: Resilient and Highly Available Remote Memory
Youngmoon Lee, Hasan Al Maruf, Mosharaf Chowdhury +2
We present Hydra, a low-latency, low-overhead, and highly available resilience mechanism for remote memory. Hydra can access erasure-coded remote memory within a single-digit micro…
Near Optimal Coflow Scheduling in Networks
Mosharaf Chowdhury, Samir Khuller, Manish Purohit +2
The coflow scheduling problem has emerged as a popular abstraction in the last few years to study data communication problems within a data center. In this basic framework, each co…