Principle:Alibaba ROLL Distributed Worker Cluster Management

Knowledge Sources	Ray Documentation Ray: A Distributed Framework Alibaba ROLL
Domains	Distributed_Systems, GPU_Computing
Last Updated	2026-02-07 20:00 GMT

Overview

A distributed systems principle for managing groups of GPU workers as named clusters with resource-aware placement, method dispatch, and lifecycle management.

Description

Distributed Worker Cluster Management addresses the challenge of coordinating multiple GPU workers (actors, critics, references, reward models) across a multi-node cluster. Each logical role (e.g., "actor_train", "reward-math") is represented as a Cluster — a named group of Ray actors that share the same worker class and configuration. The cluster abstraction provides:

• Resource-aware placement: Workers are assigned to specific GPUs via placement groups, ensuring co-location for communication-intensive operations
• Uniform method dispatch: Methods can be executed on rank-0, all workers, or in data-parallel patterns
• Lifecycle management: Workers are created, initialized, and destroyed as a unit

This principle is fundamental to all ROLL pipelines — every pipeline creates multiple clusters for different roles.

Usage

Use this principle when:

• Deploying multiple worker roles (actor, critic, reference, reward) across a GPU cluster
• Requiring data-parallel or model-parallel execution patterns
• Managing GPU resource allocation with placement groups for co-location guarantees

Theoretical Basis

The design follows a role-based actor model:

• Cluster = Named Group of Workers: Each cluster maps to a distributed role (actor_train, critic, etc.)
• ResourceManager = Global GPU Allocator: Tracks all available GPUs and assigns placement groups
• Dispatch Patterns: Methods are dispatched using configurable patterns:
  • ONE_TO_ALL: Broadcast to all workers
  • DP_MP_DISPATCH_FIRST: Data-parallel sharding with model-parallel replication
  • DP_MP_COMPUTE: Distributed computation without data collection

Pseudo-code:

# Abstract cluster lifecycle
resource_mgr = ResourceManager(num_gpus_per_node=8, num_nodes=4)
cluster = Cluster(name="actor_train", worker_cls=ActorWorker, resource_manager=resource_mgr, config=worker_config)
cluster.execute_all_sync("initialize", pipeline_config)  # Initialize all workers
results = cluster.execute_all_sync("train_step", batch)   # Distributed training

Related Pages

Implemented By

• Implementation:Alibaba_ROLL_Cluster

Related Heuristics

The following heuristics inform this principle:

• Heuristic:Alibaba_ROLL_GPU_Memory_Offload_Strategy