parallel-computing
Parallel Computing
Use this skill to convert parallel performance work into reproducible scaling decisions.
Workflow
- Define scaling objective and constraints.
- Capture workload shape, data size, and latency/throughput targets.
- Define hardware assumptions (core count, SMT policy, NUMA context).
- 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.
- Diagnose bottlenecks.
- Check lock contention, false sharing, synchronization frequency, and memory bandwidth pressure.
- Separate algorithmic limits from runtime/scheduler overhead.
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