We hear a ton of news around GPUs, especially if you’re trying to learn more or use them in your frequent ML builds. As data scientists and developers, we're accustomed to hearing about the remarkable speedups GPUs provide in deep learning workloads. Look, GPUs are indeed workhorses, allowing us to train models that were unthinkable a decade ago. But there's a lesser-known and often frustrating bottleneck that many of us have encountered: GPU memory fragmentation. I ran into it the first time and I learnt some bits about it. In this blog I have tried to explain this to help other data scientists and ML engineers find a way around, perhaps I m sure many will find a better way around than what I have covered in this blog. If you do so, please tag me or share it with me. Now, back to the article.

What is GPU Memory Fragmentation?

It’s kind of like a deadlock situation but not exactly that. When we talk about memory fragmentation, we’re talking about situations where there's enough total free memory to load a tensor, model, or batch, but that memory is split into smaller blocks. As a result, your GPU will throw a CUDA out of memory error, even though the total memory isn't fully used.

To illustrate, imagine trying to fit a new batch of data into your GPU. You might have 4GB of free memory, but it's fragmented into smaller chunks (e.g., 1GB, 1GB, and 2GB). If the new data batch requires 3GB, it won't fit despite having the total memory available. This is the GPU equivalent of having room for another suitcase in your car trunk, but it's scattered across the seats and corners.

Memory fragmentation can hit you hard, especially when experimenting with large models or dynamically altering the batch size during training. It’s a subtle challenge that can make your GPU’s life difficult and frustrate you with unexpected memory allocation errors.

Why It Happens

GPUs allocate memory in blocks, and unlike CPU memory, there's no built-in garbage collection for cleaning up fragmented blocks of memory. Over time, as you load and unload different-sized tensors, your memory gets fragmented. The larger the model and batch size, the more severe the fragmentation becomes.

It’s common in projects where:

• You’re working with variable batch sizes.

• You're performing dynamic loading of large models or switching between models.

• You perform extensive augmentation or on-the-fly modifications to your data pipeline.

A Real-World Scenario

Let’s take a practical scenario. Suppose you're working on training a deep learning model, and suddenly you hit a memory allocation error on your GPU:

RuntimeError: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 16.00 GiB total capacity; 14.00 GiB already allocated; 500.00 MiB free; 13.25 GiB reserved in total by PyTorch)

Wait a minute! You check your GPU usage, and you see this:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.142.00    Driver Version: 450.142.00    CUDA Version: 11.2   |
|-----------------------------------------------------------------------------|
| GPU  Name        Memory-Usage       Total        Used       Free     ...    |
|-----------------------------------------------------------------------------|
| 0   Tesla V100   16160MiB       13750MiB    500MiB           ...           |
+-----------------------------------------------------------------------------+

This shows that you have 16GB of memory in total, 14GB is allocated, and you have about 500MB of free memory. But when you try to load a batch that requires 512MB, you get an error. Classic GPU memory fragmentation!

How To Manage GPU Memory Fragmentation

1. Gradient Accumulation

One practical solution to memory fragmentation is gradient accumulation. Instead of increasing the batch size to fit the GPU memory, split the batch across several smaller ones and accumulate gradients over multiple forward passes. This technique reduces peak memory usage, which can alleviate fragmentation.

Here’s a simple example of gradient accumulation:

# Assume optimizer, model, and loss_fn are predefined
accumulation_steps = 4
optimizer.zero_grad()

for i, batch in enumerate(data_loader):
    outputs = model(batch)
    loss = loss_fn(outputs, labels)
    loss = loss / accumulation_steps
    loss.backward()

    # Perform step only after N batches
    if (i + 1) % accumulation_steps == 0:
        optimizer.step()
        optimizer.zero_grad()

This code breaks down the larger batch into smaller ones, reducing the chances of memory fragmentation since we're not loading a huge chunk all at once.

2. Memory Defragmentation with torch.cuda.empty_cache()

You can manually release unused memory by using torch.cuda.empty_cache(). This doesn’t free up any tensors but allows PyTorch to clear unused memory from earlier allocations. This can mitigate the problem somewhat but is not a complete solution for fragmentation.

import torch
torch.cuda.empty_cache()

It’s a good practice to call this in between validation and training phases or during periods where large memory is no longer required, such as after each epoch.

3. Use torch.cuda.memory_reserved() and torch.cuda.memory_allocated()

Monitoring your GPU memory is essential to detect fragmentation early. PyTorch provides two handy functions:

• torch.cuda.memory_reserved(): Displays the total amount of memory reserved by PyTorch.

• torch.cuda.memory_allocated(): Shows the current allocated memory.

Use them to keep track of your memory footprint and fragmentation trends.

import torch
print(f"Memory reserved: {torch.cuda.memory_reserved() / 1024 ** 3:.2f} GB")
print(f"Memory allocated: {torch.cuda.memory_allocated() / 1024 ** 3:.2f} GB")

If you see a large discrepancy between reserved and allocated memory, it's a sign of memory fragmentation.

4. Optimize Model Architecture

Consider switching to more memory-efficient layers or precision types:

• Mixed Precision Training: Reduce memory usage by switching to mixed precision, where floating point operations use half precision (16-bit) rather than full precision (32-bit). PyTorch's torch.cuda.amp package makes this easier.

# Automatic mixed precision
from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()

for data in data_loader:
    optimizer.zero_grad()

    with autocast():
        outputs = model(data)
        loss = loss_fn(outputs, labels)

    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

• Layer Normalization vs. Batch Normalization: If your data isn’t coming in mini-batches, consider replacing BatchNorm with LayerNorm, which consumes less memory and is less prone to fragmentation.

5. Pin Memory in Data Loaders

Using pinned memory helps the CPU transfer data to the GPU more efficiently, minimizing the risk of unnecessary memory retention during data transfer.

from torch.utils.data import DataLoader

data_loader = DataLoader(dataset, batch_size=32, pin_memory=True)

6. Disable Memory Caching in PyTorch

In extreme cases, you can disable PyTorch’s memory caching behavior altogether, though this is usually not recommended except in controlled debugging situations.

import os
os.environ["PYTORCH_NO_CUDA_MEMORY_CACHING"] = "1"

GPU memory fragmentation is a silent but highly common challenge when working with deep learning workloads. By leveraging techniques like gradient accumulation, mixed precision training, manual memory cleanup, and careful monitoring, we can mitigate the issues that arise from memory fragmentation. The goal is to prevent unnecessary memory allocations that can cripple GPU performance and lead to frustrating runtime errors.

Next time you encounter a CUDA memory issue, don’t just reach for the batch size reduction — consider whether fragmentation is the true culprit. Mastering memory management will make you not only a more efficient data scientist but also a happier one.

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Have you faced memory fragmentation in your GPU workloads? Let me know how you tackled it!