Model Parallelism vs Data Parallelism: Examples

Model Parallelism vs Data Parallelism

Last updated: 19th April, 2024

Model parallelism and data parallelism are two strategies used to distribute the training of large machine-learning models across multiple computing resources, such as GPUs. They form key categories of multi-GPU training paradigms. These strategies are particularly important in deep learning, where models and datasets can be very large.

What’s Data Parallelism?

In data parallelism, we break down the data into small batches. Each GPU works on one batch of data at a time. It calculates two things: the loss, which tells us how far off our model’s predictions are from the actual outcomes, and the loss gradients, which guide us on how to adjust the model’s internal settings or weights to improve predictions. Once all GPUs finish their tasks, the calculated gradients are gathered and used to update the model’s weights, making it ready for the next round of learning. This way, even with limited resources, the model can be efficiently trained on large datasets.

In data parallelism, the model itself remains intact on each computing device, but the dataset is divided into smaller batches. Each device works on a different subset of the data but with an identical copy of the model.

The communication happens when the gradients (which are calculated by each device independently) are aggregated across all devices to update the model weights. This usually happens after each batch or a group of batches has been processed.

Data parallelism scales well with the number of data samples and is particularly effective when the model size is not too large to fit into a single device’s memory.

An advantage of data parallelism over model parallelism is that the GPUs can run in parallel

What’s Model Parallelism?

In model parallelism, the model itself is divided across different GPUs, meaning different parts of the model (e.g., layers or groups of neurons) are located on GPUs. The computation happens sequentially. The communication involves the transfer of intermediate outputs (activations) between devices as the data progresses through the model. This is because different parts of the input data need to be processed by different parts of the model residing on different devices.

Model parallelism is particularly useful when the model is too large to fit into the memory of a single device. However, the efficiency of model parallelism can be limited by the overhead of inter-device communication.

Differences between Model Parallelism & Data Parallelism

The differences between model parallelism and data parallelism can be summarized as the following:

FeatureData ParallelismModel Parallelism
DefinitionThe Data is split across devices while the model is copied across devices, and each model works on a different subset of the data.The model is split across devices, with each part working on the same data but different parts of the model.
CommunicationInvolves aggregating gradients across all devices to update model weights.Involves transferring intermediate outputs between devices as data progresses through the model.
ScalabilityWorks well when increasing dataset size, especially if the model size is not too large.Useful for very large models that don’t fit into the memory of a single device.
Use CasesIdeal for large datasets with smaller to moderately sized models.Best suited for training very large models, regardless of dataset size.
Main ChallengeManaging the synchronization and aggregation of gradients from all devices.Handling the communication overhead due to the transfer of intermediate outputs between devices.
ObjectiveTo handle large datasets by distributing data.To manage large model sizes by distributing the model’s architecture.

You might want to check out Machine Learning Q and AI by Sebastian Rashka for an interesting read.

Quick Tutorial on the Difference between Model, Data & Tensor Parallelism

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog,
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