Editing Amdahl's law (section)

== Limitations ==
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Followings are limitations of Amdahl's law:<ref>{{Cite book |last=Amdahl |first=Gene M. |chapter=Validity of the single processor approach to achieving large scale computing capabilities |date=1967-04-18 |title=Proceedings of the April 18-20, 1967, spring joint computer conference on - AFIPS '67 (Spring) |chapter-url=https://dl.acm.org/doi/10.1145/1465482.1465560 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=483–485 |doi=10.1145/1465482.1465560 |isbn=978-1-4503-7895-6}}</ref><ref name=":1">{{Cite book |title=Computer Architecture: A Quantitative Approach |date=2003 |publisher=Morgan Kaufmann |isbn=978-8178672663}}</ref><ref>{{Cite book |title=Parallel Computer Architecture A Hardware/Software Approach |date=1999 |publisher=Elsevier Science |isbn=9781558603431}}</ref>

* '''Assumption of Fixed Workload''': Amdahl's Law assumes that the workload remains constant. It doesn't account for dynamic or increasing workloads, which can impact the effectiveness of parallel processing.
* '''Overhead Ignored''': Amdahl's Law neglects overheads associated with [[Concurrency (computer science)|concurrency]], including coordination, [[Synchronization (computer science)|synchronization]], [[inter-process communication]], and [[concurrency control]]. Notably, merging data from multiple [[Thread (computing)|threads]] or [[Process (computing)|processes]] incurs significant overhead due to [[conflict resolution]], [[data consistency]], versioning, and synchronization.<ref>{{Cite book |title=Concurrent Programming: Algorithms, Principles, and Foundations |date=23 December 2012 |publisher=Springer |isbn=978-3642320262}}</ref>
* '''Neglecting extrinsic factors:''' Amdahl's Law addresses computational parallelism, neglecting extrinsic factors such as data persistence, I/O operations, and memory access overheads, and assumes idealized conditions.
* '''Scalability Issues''': While it highlights the limits of parallel speedup, it doesn't address practical scalability issues, such as the cost and complexity of adding more processors.
* '''Non-Parallelizable Work''': Amdahl's Law emphasizes the non-parallelizable portion of the task as a bottleneck but doesn't provide solutions for reducing or optimizing this portion.
* '''Assumes Homogeneous Processors''': It assumes that all processors are identical and contribute equally to speedup, which may not be the case in heterogeneous computing environments.

Amdahl's law applies only to the cases where the problem size is fixed. In practice, as more computing resources become available, they tend to get used on larger problems (larger datasets), and the time spent in the parallelizable part often grows much faster than the inherently serial work. In this case, [[Gustafson's law]] gives a less pessimistic and more realistic assessment of the parallel performance.<ref>{{cite book |last1=McCool |first1=Michael |title=Structured Parallel Programming: Patterns for Efficient Computation |last2=Reinders |first2=James |last3=Robison |first3=Arch |publisher=Elsevier |year=2013 |isbn=978-0-12-415993-8 |pages=61}}</ref>

[[Neil J. Gunther#Universal Scalability Law|Universal Scalability Law]] (USL), developed by [[Neil J. Gunther]], extends the Amdahl's law and accounts for the additional overhead due to [[inter-process communication]]. USL quantifies scalability based on parameters such as contention and coherency.<ref>{{Cite book |last=Gunther |first=Neil |title=Guerrilla Capacity Planning: A Tactical Approach to Planning for Highly Scalable Applications and Services |year=2007 |isbn=978-3540261384}}</ref>