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Programming patterns and runtime support for executing concurrent tasks within event-loop architectures.
Distinguishing note: Focuses on asynchronous method execution within distributed actors, distinct from standard language-level async.
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Ray is a distributed computing framework designed to scale Python and Java applications across clusters by abstracting task scheduling and resource management. It functions as a resource-aware execution engine that manages task dependencies, placement, and fault tolerance across networked compute nodes. At its core, the system provides a stateful actor model, allowing developers to define classes that run in dedicated processes to maintain and mutate internal state across remote method calls. The framework distinguishes itself through a robust cross-language interoperability layer, enabling f
Ray allows creating actors with asynchronous methods to execute multiple tasks concurrently on a single event loop during I/O operations.