19 citations · 38 across the 3 of their papers we have counts for
3 papers
Good Intentions: Adaptive Parameter Management via Intent Signaling
Alexander Renz-Wieland, Andreas Kieslinger, Robert Gericke +3
Parameter management is essential for distributed training of large machine learning (ML) tasks. Some ML tasks are hard to distribute because common approaches to parameter managem…
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access
Alexander Renz-Wieland, Rainer Gemulla, Zoi Kaoudi +1
Parameter servers (PSs) facilitate the implementation of distributed training for large machine learning tasks. In this paper, we argue that existing PSs are inefficient for tasks…
Dynamic Parameter Allocation in Parameter Servers
Alexander Renz-Wieland, Rainer Gemulla, Steffen Zeuch +1
To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks. Parameter servers ease the implementati…