Optimizing Data Transfer in Distributed AI/ML Training Wo...

What’s Happening

Here’s the thing: A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems – part 3 The post Optimizing Data Transfer in Distributed AI/ML Training Workloads appeared first on Towards Data Science.

This is the third part of a series of posts on optimizing data transfer using NVIDIA Nsight™ Systems (nsys) profiler. Part one focused on CPU-to-GPU data copies, and part two on GPU-to-CPU copies. (we’re not making this up)

In this post, we turn our attention to data transfer between GPUs.

The Details

Nowadays, it is quite common for AI/ML training — particularly of large models — to be distributed across multiple GPUs. While there are many different schemes for performing such distribution, what they all have in common is their reliance on the constant transfer of data — such as gradients, weights, statistics, and/or metrics — between the GPUs, throughout training.

As with the other types of data transfer we analyzed in our previous posts, here too, a weak implementation could easily lead to under-utilization of compute resources and the unjustified inflation of training costs. Optimizing GPU-to-GPU communication is an active area of research and innovation involving both hardware and software development.

Why This Matters

In this post, we will focus on the most common form of distributed training — data-distributed training. In data-distributed training, identical copies of the ML model are maintained on each GPU. Each input batch is evenly distributed among the GPUs, each of which executes a training step to calculate the local gradients.

The AI space continues to evolve at a wild pace, with developments like this becoming more common.

Key Takeaways

• The local gradients are then d and averaged across the GPUs, resulting in an identical gradient update to each of the model copies.
• Disclaimers The code we will in this post is intended for demonstrative purposes; please do not rely on its accuracy or optimality.
• Please do not interpret our mention of any tool, framework, library, service, or platform as an endorsement of its use.

The Bottom Line

Please do not interpret our mention of any tool, framework, library, service, or platform as an endorsement of its use. Thanks to Yitzhak Levi for his contributions to this post.

Thoughts? Drop them below.