Inter-GPU Communication

Author(s): Hira Ahmad

Originally published on Towards AI.

Introduction

Every major leap in AI over the past decade has been powered by scaling. Models have grown to billions or even trillions of parameters, datasets span millions of examples, and GPUs are deployed in clusters to keep up. But adding more GPUs alone doesn’t guarantee faster training. The real test is whether those GPUs can exchange information efficiently.

This article delves into the significance of inter-GPU communication in distributed training, highlighting how efficient data exchange underpins GPU performance. It discusses the mechanics of GPU synchronization, examines various communication technologies, and explores advanced techniques to optimize data flow. With a focus on NVIDIA’s contributions, the article underscores the evolving landscape of AI development, asserting that robust communication channels are essential for scaling AI models and ensuring swift, effective training processes.

Read the full blog for free on Medium.

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Published via Towards AI