Performance Characterization of Distributed Deep Learning Strategies: A Quantitative Evaluation of DDP, FSDP, and Parameter Server Architectures on GPU Clusters

Abstract

Efficiently scaling deep neural networks across GPU clusters requires navigating complex trade-offs between computational throughput, memory utilization, and synchronization overhead. This paper presents a unified empirical evaluation of three dominant distributed training paradigms: Distributed Data Parallel (DDP), Fully Sharded Data Parallel (FSDP), and the Parameter Server (PS) architecture. We conduct side-by-side benchmarking on both high-performance (NVIDIA A100) and commodity-class (NVIDIA A10G) clusters to isolate the impact of communication bandwidth and gang-scheduling dependencies. Our results indicate that while DDP achieves a 2-3x speedup in training throughput for standard architectures, FSDP demonstrates a 4-6x reduction in peak memory usage, validating its utility for memory-constrained environments despite higher communication latency. Furthermore, we evaluate the elasticity of the Parameter Server architecture; while Asynchronous PS reduced training time by up to 28% compared to synchronous approaches, it incurred significant accuracy penalties (ranging from 4% to 17%) due to gradient staleness. We also analyze a modified, staleness-mitigating asynchronous protocol, which we found introduced synchronization overheads that negated throughput gains. These findings provide a decision framework for system designers, highlighting that while DDP remains optimal for homogeneous, gang-scheduled clusters, FSDP and PS offer critical alternatives for memory-bound and heterogeneous environments respectively.