Meaning
This expression computes the parallel efficiency of a program by dividing the observed speedup by the number of processor cores used. It helps quantify how well a workload scales as more cores are added, highlighting diminishing returns. Use it when evaluating the performance of multi‑threaded or distributed applications.
Primary Function
Performance analysis
Communicative Purpose
Enables assessment of parallel scaling efficiency
Pattern
efficiency = speedup / num_cores
Core Structure
... = ... / ...
Função primária
Performance analysis
Propósito comunicativo
Enables assessment of parallel scaling efficiency
Situações de gatilho
High-performance computing: evaluating speedup of a simulation across multiple cores; Web services: measuring request handling throughput when scaling server instances
Contextos
Parallel computing libraries (e.g., OpenMP, MPI), performance benchmarking suites, cloud auto‑scaling services
Padrão
efficiency = speedup / num_cores
Estrutura central
... = ... / ...
Slots de substituição
efficiency: float result, speedup: float observed speedup, num_cores: int number of cores (must be >0)
Colocados típicos
- speedup
- num_cores
- parallel_efficiency
- scaling_factor
Substituições comuns
- efficiency = (runtime_single / runtime_multi) / num_cores – uses runtimes instead of speedup
- efficiency = speedup * (1 / num_cores) – same calculation expressed as multiplication
Erros comuns
Dividing by zero when num_cores is zero → runtime error; Confusing speedup with throughput → misinterpreted efficiency; Using integer division in languages with integer types → truncated result; Forgetting to convert percentages to fractions → inflated efficiency values
Similar / contraste
Speedup alone – measures only performance gain; Amdahl's Law – predicts maximum speedup given a serial fraction
Interferências
Coming from Python: using // for division causes integer truncation → use / for float division
Família do chunk
- speedup
- scalability
- Amdahl's Law
- Gustafson's Law
Nuance
Do not use for single‑threaded code where efficiency is always 1; Division overhead is negligible but integer division can truncate, affecting accuracy; Efficiency > 1 indicates superlinear scaling, which may arise from caching effects or algorithmic changes
Efeito pragmático
Helps identify scaling bottlenecks and informs decisions about adding more cores or redesigning algorithms
Dica de memória
Parallel efficiency is like fuel mileage: it tells you how far you get per core, just as miles per gallon tells you how far a car travels per unit of fuel.
Nota
Efficiency values above 1 indicate superlinear scaling, often due to cache effects or algorithmic improvements
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