138 citations · 157 across the 9 of their papers we have counts for
7 papers · 1 filter
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…
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…
BlobSeer: How to Enable Efficient Versioning for Large Object Storage under Heavy Access Concurrency
Bogdan Nicolae, Gabriel Antoniu, Luc Bougé
To accommodate the needs of large-scale distributed P2P systems, scalable data management strategies are required, allowing applications to efficiently cope with continuously growi…
Distributed Management of Massive Data: an Efficient Fine-Grain Data Access Scheme
Bogdan Nicolae, Gabriel Antoniu, Luc Bougé
This paper addresses the problem of efficiently storing and accessing massive data blocks in a large-scale distributed environment, while providing efficient fine-grain access to d…