When it comes to training large AI models, one of the main questions is: how should we allocate computational resources among tasks? Can a task be run in parts as resources become available, or should you wait until everything required is free at the same time?
This choice between rigidity and flexibility is the foundation of a technology called Gang Scheduling. Simply put, it's a resource allocation method where a task either runs completely or not at all.
Почему мы не можем просто запускать задачи по частям?
Why Can't We Just Run Tasks in Parts?
Imagine you're training a large neural network. This requires using several dozen or even hundreds of GPUs simultaneously. If you run the task partially – say, on half the required devices – the others will sit idle, waiting for the missing resources. It's like assembling half an orchestra and asking them to play a symphony: the musicians are there, but they're useless until the rest arrive.
This situation is called a «deadlock». A task occupies resources but can't start, thereby blocking other tasks that also need those resources. As a result, the system freezes, and computational power is wasted.
Gang Scheduling solves this problem radically: a task is launched only when all its required resources are available. If anything is missing, the task waits in a queue. This is the «rigidity» of the approach – no compromises, it's all or nothing.
Откуда появилась идея Gang Scheduling?
Where Did the Idea of Gang Scheduling Come From?
The concept of Gang Scheduling itself is not new. Its roots go back to the 1990s, when researchers were working on parallel computing in supercomputers. Back then, tasks also required simultaneous access to multiple processors, and it became clear even then that partial launches were a bad idea.
Today, this idea is experiencing a renaissance in the context of machine learning. Modern distributed systems for training models face the same problems as supercomputers did 30 years ago, only on a much larger scale.
Как это работает в современных системах
How It Works in Modern Systems
In the Kubernetes ecosystem – one of the most popular platforms for managing containerized applications – Gang Scheduling is implemented through specialized schedulers. One such project is called Koordinator.
The essence is that the scheduler analyzes the current state of the cluster: how many GPUs are free, which nodes are available, and what tasks are already running. It then makes a decision: can the new task be launched in its entirety, or should it wait? If resources are insufficient, the task remains in the queue for a better time.
This helps avoid situations where half the cluster is occupied by partially launched tasks that are waiting for missing resources. Instead, the system operates predictably: a task is either running or explicitly waiting its turn.
Где нужна гибкость
Where Flexibility Is Needed
But a rigid approach isn't always optimal. Sometimes it makes sense to deviate slightly from the «all or nothing» principle. For example, if a task can be scaled – meaning it can run on a variable number of devices at different speeds – it could be launched with fewer GPUs, and more can be added later as resources become available.
This is «elasticity». It allows for more efficient use of available resources without waiting for the perfect configuration. But it's important to understand that not all tasks support this kind of flexibility. For many distributed training algorithms, changing the number of workers on the fly is a non-trivial task that requires additional logic and synchronization.
Баланс жесткости и адаптивности
The Balance Between Rigidity and Adaptability
Modern orchestration systems try to find a happy medium. On one hand, Gang Scheduling ensures that tasks don't get stuck in a half-launched state. On the other, elastic mechanisms can be used when possible to prevent free resources from sitting idle.
For example, a task could be launched with a minimum required set of GPUs, and then resources can be added dynamically as they become available. Or, conversely, you could temporarily «take back» some resources from a low-priority task to allow a more important one to launch.
This approach requires more complex scheduling logic, but it allows for more efficient cluster utilization, especially under high load.
Что дальше?
What's Next?
The development of Gang Scheduling and its related technologies is moving in several directions. First, integration with various machine learning frameworks is being improved, so the system can automatically understand a task's requirements and make decisions without manual configuration.
Second, smarter queuing algorithms are emerging: not just «first-come, first-served», but ones that account for priorities, deadlines, the cost of downtime, and other factors.
Third, there is growing interest in hybrid approaches that combine the rigidity of Gang Scheduling with the elasticity of dynamic scaling. This is especially important for cloud providers who need to maximize the use of every GPU without sacrificing task execution reliability.
The question of allocating computational power for AI is becoming increasingly relevant as models grow and infrastructure becomes more complex. And technologies like Gang Scheduling are not just technical details, but a fundamental choice between predictability and flexibility, between guarantees and resource efficiency.