138 citations · 157 across the 9 of their papers we have counts for
9 papers
Efficient Data-Parallel Continual Learning with Asynchronous Distributed Rehearsal Buffers
Thomas Bouvier, Bogdan Nicolae, Hugo Chaugier +3
Deep learning has emerged as a powerful method for extracting valuable information from large volumes of data. However, when new training data arrives continuously (i.e., is not fu…
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…
ProvLight: Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum
Daniel Rosendo, Marta Mattoso, Alexandru Costan +4
Modern scientific workflows require hybrid infrastructures combining numerous decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the Computing Continu…
Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review
Daniel Rosendo, Alexandru Costan, Patrick Valduriez +1
The explosion of data volumes generated by an increasing number of applications is strongly impacting the evolution of distributed digital infrastructures for data analytics and ma…
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…