In this article, we’re going to take a closer look into the dark arts of GPU memory management a topic that may make some developers cringe but is crucial for getting the most out of your deep learning models. We’ll explore various techniques for optimizing memory allocation on NVIDIA GPUs, including best practices for data loading and model architecture design.

To kick things off: why memory management matters in deep learning. The answer lies in a little-known fact the majority of time spent training or inferencing with a neural network is actually spent moving data between CPU and GPU memory. This can be due to several factors, such as large input sizes, small batch sizes, or complex model architectures that require frequent parameter updates.

To illustrate this point, let’s take a look at the following example: suppose we have a simple convolutional neural network (CNN) with an input size of 256×256 pixels and a batch size of 32. If we train this model for just one epoch on a single NVIDIA Tesla V100 GPU, it can take up to several hours not because the computations are particularly complex or time-consuming, but rather due to the sheer amount of data that needs to be moved between CPU and GPU memory.

To optimize this process, we need to focus on two key areas: data loading and model architecture design. Let’s start with data loading specifically, how we can minimize the number of times we have to move data back and forth between CPU and GPU memory during training or inference.

One technique for achieving this is called “data parallelism,” which involves splitting a large dataset into smaller chunks that can be processed simultaneously on multiple GPUs. This not only reduces the amount of time spent moving data, but also allows us to train larger models with more data both of which are crucial for achieving state-of-the-art results in deep learning.

Another technique is called “model parallelism,” which involves splitting a large model into smaller sub-models that can be processed simultaneously on multiple GPUs. This not only reduces the amount of time spent moving parameters, but also allows us to train larger models with more layers both of which are crucial for achieving state-of-the-art results in deep learning.

But what about memory allocation itself? How do we ensure that our data and model parameters are stored efficiently on GPU memory? The answer lies in a little-known fact: NVIDIA GPUs have two types of memory global memory (which is slower but has higher capacity) and shared memory (which is faster but has lower capacity).

To optimize memory allocation, we need to focus on minimizing the amount of data that needs to be stored in global memory while maximizing the use of shared memory. This can be achieved through various techniques, such as using smaller input sizes or batch sizes, or designing models with fewer parameters.

Another technique is called “model quantization,” which involves converting floating-point model weights and activations into fixed-point values (such as 8-bit integers) to reduce the amount of memory required for storage and computation. This not only reduces the size of our models, but also allows us to train them faster on smaller GPUs both of which are crucial for achieving state-of-the-art results in deep learning.