5 citations · 9 across the 5 of their papers we have counts for
5 papers
Keigo: Co-designing Log-Structured Merge Key-Value Stores with a Non-Volatile, Concurrency-aware Storage Hierarchy (Extended Version)
Rúben Adão, Zhongjie Wu, Changjun Zhou +3
We present Keigo, a concurrency- and workload-aware storage middleware that enhances the performance of log-structured merge key-value stores (LSM KVS) when they are deployed on a…
HarMoEny: Efficient Multi-GPU Inference of MoE Models
Zachary Doucet, Rishi Sharma, Martijn de Vos +3
Mixture-of-Experts (MoE) models offer computational efficiency during inference by activating only a subset of specialized experts for a given input. This enables efficient model s…
Accelerating MoE Model Inference with Expert Sharding
Oana Balmau, Anne-Marie Kermarrec, Rafael Pires +3
Mixture of experts (MoE) models achieve state-of-the-art results in language modeling but suffer from inefficient hardware utilization due to imbalanced token routing and communica…
Practical Federated Learning without a Server
Akash Dhasade, Anne-Marie Kermarrec, Erick Lavoie +3
Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coo…
Limiting the Spread of Fake News on Social Media Platforms by Evaluating Users' Trustworthiness
Oana Balmau, Rachid Guerraoui, Anne-Marie Kermarrec +3
Today's social media platforms enable to spread both authentic and fake news very quickly. Some approaches have been proposed to automatically detect such "fake" news based on thei…