parallel-computing

Installation
SKILL.md

Parallel Computing

Use this skill to convert parallel performance work into reproducible scaling decisions.

Workflow

  1. Define scaling objective and constraints.
  • Capture workload shape, data size, and latency/throughput targets.
  • Define hardware assumptions (core count, SMT policy, NUMA context).
  1. Choose parallel model and partitioning.
  • Select task/data/pipeline parallelism intentionally.
  • Set chunk size and scheduling strategy to minimize overhead and imbalance.
  • Define shared-state boundaries before coding.
  1. Diagnose bottlenecks.
  • Check lock contention, false sharing, synchronization frequency, and memory bandwidth pressure.
  • Separate algorithmic limits from runtime/scheduler overhead.
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Mar 10, 2026