Skip to content

Distributed frank-wolfe under pipelined stale synchronous parallelism

We are witnessing the move towards data center operating systems (OS), where resources are unified and  processing frameworks coexist with each other. In this context it has been shown that an iteration model with relaxed consistency such as the Stale Synchronous Parallel (SSP) model, while still guaranteeing convergence, is able to cope with the straggler problem for converging iterative algorithms. In this poster we present a model for the integration of the SSP model on a pipelined processing framework. We then apply the SSP on a distributed version of the Frank-Wolfe algorithm and empirically show its convergence under stress situations similar to those encountered in a data center OS.

 

Thomas Peel, and Nam-Luc Tran, Distributed Frank-Wolfe under Pipelined Stale Synchronous Parallelism, poster at the Greed is Great ICML’15 Workshop, Lille, France, July 2015

Releated Posts

Insights from GTC Paris 2025

Among the NVIDIA GTC Paris crowd was our CTO Sabri Skhiri, and from quantum computing breakthroughs to the full-stack AI advancements powering industrial digital twins and robotics, there is a lot to share! Explore with Sabri GTC 2025 trends, keynotes, and what it means for businesses looking to innovate.
Read More

Development & Evaluation of Automated Tumour Monitoring by Image Registration Based on 3D (PET/CT) Images

Tumor tracking in PET/CT is essential for monitoring cancer progression and guiding treatment strategies. Traditionally, nuclear physicians manually track tumors, focusing on the five largest ones (PERCIST criteria), which is both time-consuming and imprecise. Automated tumor tracking can allow matching of the numerous metastatic lesions across scans, enhancing tumor change monitoring.
Read More