Marius is a system under active development for training embeddings for large-scale graphs on a single machine. Training on large scale graphs requires a large amount of data movement to get embedding parameters from storage to the computational device.Marius is designed to mitigate/reduce data movement overheads using: Pipelined training and IO Partition caching and buffer-aware data orderings Details on how Marius works can be found in our OSDI ’21 Paper, where experiment scripts and configurations can be found in the […]

FedScale This repository contains scripts and instructions of building FedScale, a diverse set of challenging and realistic benchmark datasets to facilitate scalable, comprehensive, and reproducible federated learning (FL) research. FedScale datasets are large-scale, encompassing a diverse range of important FL tasks, such as image classification, object detection, language modeling, speech recognition, and reinforcement learning. For each dataset, we provide a unified evaluation protocol using realistic data splits and evaluation metrics. To meet the pressing need for reproducing realistic FL at […]