138 citations · 288 across the 18 of their papers we have counts for
8 papers · 1 filter
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices
Ji Liu, Tianshi Che, Yang Zhou +4
Federated Learning (FL) has achieved significant achievements recently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or model exc…
KheOps: Cost-effective Repeatability, Reproducibility, and Replicability of Edge-to-Cloud Experiments
Daniel Rosendo, Kate Keahey, Alexandru Costan +3
Distributed infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex scientific workflows to be executed across hybrid sy…
Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources
Ji Liu, Daxiang Dong, Xi Wang +5
Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which ex…
Enabling Reproducible Analysis of Complex Workflows on the Edge-to-Cloud Continuum
Daniel Rosendo, Alexandru Costan, Gabriel Antoniu +1
Distributed digital infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed from IoT Edge de…
Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum
Daniel Rosendo, Alexandru Costan, Gabriel Antoniu +4
In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infra…
Distributed In-memory Data Management for Workflow Executions
Renan Souza, Vítor Silva, Alexandre A. B. Lima +3
Complex scientific experiments from various domains are typically modeled as workflows and executed on large-scale machines using a Parallel Workflow Management System (WMS). Since…
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering
Renan Souza, Leonardo Azevedo, Vítor Lourenço +10
Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of…
Parallel Computation of PDFs on Big Spatial Data Using Spark
Ji Liu, Noel Moreno Lemus, Esther Pacitti +2
We consider big spatial data, which is typically produced in scientific areas such as geological or seismic interpretation. The spatial data can be produced by observation (e.g. us…