When it comes to training large language models, the first thing developers think of is computational resources: hundreds and thousands of GPUs working simultaneously. However, simply having powerful hardware doesn't guarantee its effective use. One of the main challenges at this scale is idle time – moments when some hardware literally waits for other parts to finish their work.
This is precisely the problem that AMD's new development, Primus, addresses – a flexible implementation of what's known as pipeline parallelism. Let's break down what that means and why it's important.
Why Pipeline Parallelism is Needed
Why Is Pipeline Parallelism Needed?
Imagine you're training a model so large it doesn't fit into the memory of a single GPU. It has to be 'sliced' into parts and distributed across multiple devices. Each device processes its part of the model, and the data 'flows' through them sequentially – like on a factory assembly line.
The problem is that a classic pipeline operates unevenly. While one GPU is performing computations, others are waiting. These pauses are called «bubbles» – they reduce the overall efficiency of the system and turn expensive hardware into a partially idle resource.
The more GPUs involved and the deeper the model, the more serious this problem becomes. For large models, the losses from these «bubbles» can be very significant.
What Zero Bubbles Are and Their Complexities
What Are 'Zero Bubbles,' and Why They're Not as Simple as They Sound
In recent years, a whole class of algorithms has emerged under the general name zero-bubble. The idea is to reorder computations so that devices don't idle: while one part of the model is waiting for results from an adjacent GPU, it can be doing something else – for example, calculating gradients for the previous step.
Sounds logical, but implementing it is not simple. Different tasks require different algorithm variants, and until now, most systems either supported only one or two options or required significant modifications for a specific configuration.
This is where Primus comes in.
What Primus Offers
Primus is a pipeline parallelism implementation within Primus Megatron-LM, a backend from AMD built on top of Megatron-LM – one of the most widely used frameworks for training large models.
The key difference between Primus and most existing solutions is its support for a full suite of zero-bubble algorithms within a single system. This means developers don't have to choose one approach and 'live with it' – they can switch between variants depending on the task.
The following modes are supported:
- zerobubble – the basic zero-bubble algorithm;
- zbv – a variant with interleaving, meaning a finer-grained slicing of tasks between devices;
- v-half – a compromise between efficiency and memory consumption;
- v-min – a mode with minimal memory consumption.
Simply put, it's like a set of gears in a car: different conditions call for different modes. Primus gives you the ability to choose – and to switch.
Why Flexibility is Crucial Here
Why Flexibility Is More Important Here Than It Seems
In practice, training large models is always a compromise. If you want fewer 'bubbles,' you pay with memory. If you want to save memory, you accept the pauses. There's no one-size-fits-all solution.
Most implementations force you to choose one option during the development or system configuration phase. Primus offers a different approach: a single, unified engine where switching between algorithms is a matter of configuration, not rewriting code.
For teams that train models at different scales and with varying requirements, this is significant. There's no need to maintain multiple separate pipelines or adapt the system to new conditions every time.
How It Works Technically
How It Works Technically – Without Getting Into Too Much Detail
Inside Primus is a component known as a scheduler, which decides what computations are performed on each device and in what order. It's responsible for ensuring devices are utilized as much as possible and that idle time is minimized.
This scheduler is written to be easily extensible: a new algorithm can be added without rebuilding the entire system architecture. This is important because the field of zero-bubble algorithms is actively developing, and new approaches may appear in a year or two – Primus is designed to incorporate them.
Additionally, the system supports different memory configurations and can adapt to specific hardware. This is especially relevant for AMD GPUs, for which Primus is designed.
What About Performance
What About Performance?
AMD developers provide test results on clusters with AMD Instinct GPUs. According to their data, Primus in zerobubble mode shows a significant increase in efficiency compared to the classic approach – especially on configurations with a large number of devices, where 'bubbles' are traditionally most impactful.
However, the authors frankly note that results depend on the specific model, batch size, and hardware configuration. There is no single number that fits all scenarios. This is normal – and it's a sign of a balanced presentation, not marketing hyperbole.
Who Is This For
Who Is This For?
Primus is a tool for those who train large models at an industrial scale. It's not something that would be useful for a researcher running small experiments on one or two GPUs.
The target audience is teams and organizations that:
- Work with models that require distribution across tens or hundreds of GPUs;
- Use or are considering AMD Instinct hardware in their infrastructure;
- Want to squeeze maximum performance out of their existing resources without rewriting the entire system from scratch.
For such teams, optimizing GPU utilization is literally money. Every percentage point of idle time in a cluster of hundreds of GPUs translates into real costs.
What's Next for Zero Bubble Algorithms
What's Next
Zero-bubble algorithms are not the final point in the evolution of pipeline parallelism, but rather its current cutting edge. Research continues, and new approaches appear regularly.
Primus is interesting because AMD is positioning it as an extensible platform, not just a set of specific algorithms. If the architecture genuinely allows for embedding new schedulers without major rework, it gives the system a certain degree of future-proofing.
An open question remains as to how easily Primus can be integrated into existing pipelines – especially for those already using other frameworks or wrappers around Megatron-LM. This is always a stumbling block when introducing new tools into production systems.
But the very fact that AMD is publicly developing its own stack for training large models – and doing so with an emphasis on flexibility, not just on being 'faster than before' – speaks to the maturity of its approach. We'll see how it catches on in practice. 👀