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Topic
Parallel workloads, CPU-bound work, concurrency limits, and throughput trade-offs.
Practice items tagged with Parallelism.
Uses reduction, thread-local state, atomic scalar updates, or indexed output instead of mutating a shared collection from parallel workers.
Sets concurrency from the constrained resource and verifies throughput, latency, and downstream safety instead of maximizing active work.
Serializes work for the same resource while preserving concurrency across independent keys and managing keyed-lock lifetime safely.
Decide how to handle CPU-heavy work in an API without damaging request throughput or making the system unreliable.
Diagnoses thread-unsafe scoped dependencies inside parallel execution and redesigns the data boundary without exhausting shared resources.
Defines what happens when several operations fail, cancellation arrives, or only part of a parallel batch completes, without orphaning work or hiding errors.