I’ve been trying to build a cleaner mental model for distributed compute systems instead of learning each framework independently. Kubernetes, Slurm, Ray, and Spark all use different abstractions, but many of the underlying problems are the same: scheduling, resource management, worker execution, state, communication, memory, and failure recovery. I wrote up the framework-independent model first, then mapped each system onto it. Would be interested in how others think about the boundaries between cluster scheduler, runtime, and application-level scheduler. Article: https://pawankjha.substack.com/p/the-architecture-behind-modern-distributed submitted by /u/ArchitectingAI
Originally posted by u/ArchitectingAI on r/ArtificialInteligence
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