Linked from

The 12 pages that link to Amdahl's law, each with the reason it gives.

• Parallel computingRelated: It explains why a small sequential portion can cap the benefit of adding processors.

• Moore's lawCompared with: It limits software speedups even when hardware gains add more transistors.

• High-performance computingRelated: It explains why adding processors yields diminishing returns when workloads contain serial work.

• BottleneckBroader topic: The serial fraction remains a computational bottleneck even as more processors are added.

• Concurrency (computer science)Related: It shows why concurrency cannot accelerate work that remains inherently sequential.

• Parallel algorithmRelated: It quantifies why a small sequential portion can cap parallel algorithm performance.

• Gustafson's lawCompared with: It holds problem size fixed, unlike Gustafson’s scaling assumption.

• Gene AmdahlBroader topic: Amdahl formulated this limit to show how serial work constrains parallel gains.

• Hardware accelerationRelated: It shows why accelerating only part of a program has a bounded payoff.

• IBM 7030 StretchRelated: Stretch’s overlapping operations exposed the practical limits of accelerating only parts of a computation.

• General-purpose computing on graphics processing unitsRelated: It explains why adding GPU parallelism cannot eliminate sequential bottlenecks.

• Multi-core processorRelated: It explains why adding cores yields diminishing returns when a workload has serial sections.