Global vs. Local SPMD (blog.ezyang.com)

A new discussion in the AI/ML community revolves around the distinctions between Global SPMD (Single Program Multiple Data) and Local SPMD approaches in distributed computing frameworks like JAX and PyTorch. Global SPMD allows developers to write multi-device code as though it operates on a single device, enhancing tensor distribution via both implicit and explicit mechanisms. In contrast, Local SPMD focuses on a "per-device view," requiring explicit communication methods between devices. The discourse highlights that while conventional wisdom suggests defaulting to Global SPMD for its efficiency, careful consideration is necessary when choosing between the two, as Local SPMD can introduce unique bugs if not managed correctly. The significance of this differentiation lies in its potential to impact the way developers approach distributed computation. Local SPMD, while offering greater flexibility and potentially improved performance in cases where explicit communication is needed, can lead to increased complexity and silent errors if developers are not intimately familiar with its mechanics. On the other hand, Global SPMD simplifies the development process by ensuring operations are consistent across devices. The article encourages a deeper understanding of these semantics, especially for those experimenting with distributed programming, as the choice between Global and Local SPMD can drastically influence both the development experience and the performance outcome of AI models.