NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access

TL;DR

NuPS is a new parameter server architecture designed to efficiently handle non-uniform parameter access patterns in distributed machine learning, significantly outperforming existing systems in speed and scalability.

Contribution

NuPS introduces a hybrid management approach and sampling primitives to address non-uniform access issues, improving performance over traditional parameter servers.

Findings

NuPS outperforms existing PSs by up to ten times in speed.

NuPS achieves near-linear scalability across multiple tasks.

NuPS effectively manages skew and sampling challenges.

Abstract

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 that exhibit non-uniform parameter access; their performance may even fall behind that of single node baselines. We identify two major sources of such non-uniform access: skew and sampling. Existing PSs are ill-suited for managing skew because they uniformly apply the same parameter management technique to all parameters. They are inefficient for sampling because the PS is oblivious to the associated randomized accesses and cannot exploit locality. To overcome these performance limitations, we introduce NuPS, a novel PS architecture that (i) integrates multiple management techniques and employs a suitable technique for each parameter and (ii) supports sampling directly via suitable sampling primitives and sampling schemes that allow for a controlled quality--efficiency trade-off. In our experimental study, NuPS outperformed existing PSs by up to one order of magnitude and provided up to linear scalability across multiple machine learning tasks.