Improving CNN Training Efficiency with Multiple GPUs: A Practical Benchmark

In this article, we will explore the benefits of distributing the training workload across multiple GPUs for a given batch size. Specifically, we will focus on the scenario of training a CNN like GoogleNet on the CIFAR-10 dataset with a batch size of 128. Instead of using a single powerful GPU, we will see how splitting the data across multiple GPUs can significantly reduce training time.

Introduction to Data Parallelism in CNN Training

Data parallelism is a well-known technique in deep learning where the input data is split across multiple GPUs, and each GPU processes a portion of the data. This approach is particularly useful when the model can be efficiently parallelized, leading to faster training times and reduced overall computation time. In this study, we will compare the training times of a GoogleNet model on CIFAR-10 using both a single GPU and distributed across multiple GPUs.

Experimental Setup and Benchmarks

The experimental setup involves a system with 2 Titan X GPUs and 1 GTX 1080 GPU. We will train the model on a single powerful GPU and compare it with the distributed training across 3 GPUs, where each GPU processes a portion of the batch size of 128. For the single GPU scenario, the elapsed time is 5 minutes and 25 seconds, whereas for the distributed training, the elapsed time is 2 minutes and 23 seconds.

Results: Near-Linear Speedup with Multiple GPUs

The benchmark results demonstrate a near-linear speedup with respect to the number of GPUs. Despite the communication overhead, the time reduction is substantial and makes the distributed training highly beneficial. This improvement is not only time-efficient but also showcases how current hardware and software advancements have made data parallelism a more accessible and practical solution.

Advantages of Using Multiple GPUs for Training

Using multiple GPUs for training offers several advantages:

Faster Training Times: By distributing the workload, the total training time is significantly reduced. Better Resource Utilization: High-performance GPUs can be fully utilized, ensuring efficient use of precious computational resources. Scalable Solutions: As model complexity and dataset size increase, distributing the training across multiple GPUs becomes even more crucial. Actionable for Modern Hardware: The ease of implementing data parallelism with modern frameworks like PyTorch demonstrates how contemporary software tools simplify complex tasks.

With a single line of code, you can achieve data parallelism, making the process straightforward and efficient. Here is an example of how to implement this in PyTorch:

net  Net()
net  (net, device_ids[0,1,2])

This simplicity is a testament to the advancements in software development for deep learning. In fact, in the current technological landscape, distributing the training workload is something that can be done quickly and without significant manual intervention.

Conclusion: Embracing Data Parallelism

The benchmark results underscore the significant benefits of using multiple GPUs for CNN training, especially when the batch size is large. While the use of multiple GPUs might have been a resource-intensive task in the past, modern hardware and software make it more accessible and efficient. By embracing data parallelism, researchers and practitioners can significantly enhance their model training processes and achieve faster results.

In summary, the right use of multiple GPUs can greatly speed up your training process, and it's an essential practice in modern deep learning. By understanding the principles and implementing them effectively, you can take full advantage of the latest hardware and software advancements to boost your model training speeds and improve overall efficiency.