Principle:Ray project Ray Distributed Task Submission
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Computing, Task_Scheduling |
| Last Updated | 2026-02-13 17:00 GMT |
Overview
A mechanism for submitting function invocations to a distributed scheduler for asynchronous execution on remote worker nodes, returning a future reference to the result.
Description
Distributed Task Submission is the core operation in task-parallel distributed computing. A client process submits a function reference along with its arguments to a cluster scheduler (Raylet), which assigns the task to an available worker node based on resource availability. The submission is asynchronous — it returns immediately with a future reference (ObjectRef) to the eventual result, without blocking the caller.
The submission pipeline involves:
- Resolving the function reference to a concrete method descriptor
- Serializing arguments (supporting both value and reference passing)
- Dispatching to the cluster scheduler via native bindings (JNI)
- Returning a distributed future (ObjectRef) immediately
Usage
Use this principle whenever you need to execute a function on a remote worker node in the cluster. Task submission is the primary mechanism for distributing computation across nodes. It is appropriate for stateless, embarrassingly parallel workloads where each invocation is independent.
Theoretical Basis
Distributed task submission implements the Futures/Promises pattern in a distributed setting:
The key properties are:
- Non-blocking: The caller receives a future immediately
- Location-transparent: The scheduler decides which node executes the task
- Pass-by-reference: Arguments can be ObjectRef values, enabling data dependency DAGs without data movement
Pseudo-code:
// Abstract task submission pattern
functionDescriptor = resolveFunction(f)
serializedArgs = serializeArguments(args)
objectRef = scheduler.submitTask(functionDescriptor, serializedArgs, options)
return objectRef // returns immediately