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Advanced Topic 51 of 51 Concurrency & Parallelism

Performance Scaling Analysis

Measuring speedup and efficiency as cores increase.

How it works

Performance scaling analysis studies how a program's performance changes as resources grow. Strong scaling keeps the problem size fixed and increases processors, measuring speedup and efficiency, limited by Amdahl's law; weak scaling grows the problem size proportionally with processors, aiming for constant time, as in Gustafson's law. Metrics include speedup, parallel efficiency, scalability curves, isoefficiency and the roofline model, which relates achievable performance to arithmetic intensity and memory bandwidth. Profilers and tracing identify serial sections, communication costs, load imbalance and contention.

Where it is used

HPC centers, database and web-service benchmarking, and capacity planning evaluate scaling before deployment to decide cluster size.

Common pitfall

Measuring on too few core counts or with noisy results gives misleading curves. Comparing against a poor serial baseline inflates speedups. Scaling often breaks at socket and node boundaries because of NUMA and network effects. Superlinear speedup from caching effects can be misread. Report both strong and weak scaling, repeat runs, compare with the best serial implementation and find the bottleneck rather than only the curve.

Where it fits in the course

Performance Scaling Analysis is topic 51 of 51 in Concurrency & Parallelism, rated Advanced. It follows Hardware Transactional Memory. It is the last topic in this area.

Practice and explore

How to study this topic

  1. Read the explanation above and restate the definition in your own words.
  2. Open the interactive lesson and answer its mini quiz.
  3. Read the common pitfall, then write a short example that avoids it.
  4. Check your understanding in the Concurrency & Parallelism quiz, and revisit the topic if you miss a question.

Frequently asked questions

What is Performance Scaling Analysis?
Measuring speedup and efficiency as cores increase. It belongs to Concurrency & Parallelism and is an advanced-level topic.
Where do people use Performance Scaling Analysis?
HPC centers, database and web-service benchmarking, and capacity planning evaluate scaling before deployment to decide cluster size.
What is the most common mistake with Performance Scaling Analysis?
Measuring on too few core counts or with noisy results gives misleading curves.
What should I learn before this topic?
Start with Hardware Transactional Memory, the topic just before it in Concurrency & Parallelism.
How can I practice it?
Answer the mini quiz in the interactive lesson, then take the Concurrency & Parallelism quiz.
Is Performance Scaling Analysis hard to learn?
It is an Advanced topic. Expect to read the explanation more than once and to connect it with earlier topics. Working through a small example by hand helps most.
How long does it take to learn Performance Scaling Analysis?
Most learners can read the explanation and finish the interactive lesson in under an hour. Fluency comes from applying the idea, so plan a few short practice sessions with the quiz.
Which topics are related to Performance Scaling Analysis?
Hardware Transactional Memory, Memory Models in Languages, Linearizability are closely connected. The full list is in the related topics section of this page.
What should I study after Performance Scaling Analysis?
You have reached the last topic in Concurrency & Parallelism. Pick another area on the Learn page, or revisit this topic with the quiz.

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