4 strategies for multi-GPU training explained visually.

By default, deep learning models only utilize a single GPU for training, even if multiple GPUs are available. Here's a hack.

An ideal way to proceed (especially in big-data settings) is to distribute the training workload across multiple GPUs.

The graphic below depicts four common strategies for multi-GPU training:

1. Model parallelism

• Different parts (or layers) of the model are placed on different GPUs.
• Useful for huge models that do not fit on a single GPU.
• However, model parallelism also introduces severe bottlenecks as it requires data flow between GPUs when activations from one GPU are transferred to another GPU.

2. Tensor parallelism

• Distributes and processes individual tensor operations across multiple devices or processors.
• It is based on the idea that a large tensor operation, such as matrix multiplication, can be divided into smaller tensor operations, and each smaller operation can be executed on a separate device or processor.
• Such parallelization strategies are inherently built into standard implementations of PyTorch and other deep learning frameworks, but they become much more pronounced in a distributed setting.

3. Data parallelism

• Replicate the model across all GPUs.
• Divide the available data into smaller batches, and each batch is processed by a separate GPU.
• The updates (or gradients) from each GPU are then aggregated and used to update the model parameters on every GPU.

4. Pipeline parallelism

• This is often considered a combination of data parallelism and model parallelism.

• So the issue with standard model parallelism is that 1st GPU remains idle when data is being propagated through layers available in 2nd GPU:

• Pipeline parallelism addresses this by loading the next micro-batch of data once the 1st GPU has finished the computations on the 1st micro-batch and transferred activations to layers available in the 2nd GPU.

• The process looks like this:

↳ 1st micro-batch passes through the layers on 1st GPU.

↳ 2nd GPU receives activations on 1st micro-batch from 1st GPU.

↳ While the 2nd GPU passes the data through the layers, another micro-batch is loaded on the 1st GPU.

↳ And the process continues.

• GPU utilization drastically improves this way. This is evident from the animation below where multi-GPUs are being utilized at the same timestamp (look at t=1, t=2, t=5, and t=6).

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