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Advanced Topic 40 of 51 Concurrency & Parallelism

Load Balancing in Parallel Programs

Distributing work evenly to avoid idle workers.

How it works

Load balancing distributes work evenly among processors so none sits idle while others finish, since overall time is set by the slowest worker. Static balancing assigns work in advance, by blocks or cyclically, suited to uniform tasks. Dynamic balancing assigns work at run time through shared queues, self-scheduling loops with chunk sizes as in guided scheduling and work stealing, where idle workers take tasks from busy ones. Domain decomposition and graph partitioners, such as METIS, minimize communication while balancing load in simulations.

Where it is used

OpenMP loop scheduling, task runtimes, MPI simulations with adaptive meshes, web server clusters and GPU kernels with irregular work all require balance.

Common pitfall

Perfect balance may not be possible if tasks vary unpredictably. Fine-grained dynamic scheduling adds overhead and contention for the queue, while coarse static chunks leave stragglers. Locality may suffer when work moves. Measure per-thread times, choose granularity wisely and consider over-decomposition to give the scheduler flexibility.

Where it fits in the course

Load Balancing in Parallel Programs is topic 40 of 51 in Concurrency & Parallelism, rated Advanced. It follows Parallel Sorting. The next topic is False Sharing.

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 Load Balancing in Parallel Programs?
Distributing work evenly to avoid idle workers. It belongs to Concurrency & Parallelism and is an advanced-level topic.
Where do people use Load Balancing in Parallel Programs?
OpenMP loop scheduling, task runtimes, MPI simulations with adaptive meshes, web server clusters and GPU kernels with irregular work all require balance.
What is the most common mistake with Load Balancing in Parallel Programs?
Perfect balance may not be possible if tasks vary unpredictably.
What should I learn before this topic?
Start with Parallel Sorting, 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 Load Balancing in Parallel Programs 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 Load Balancing in Parallel Programs?
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 Load Balancing in Parallel Programs?
Parallel Sorting, False Sharing, GPU Memory Hierarchy are closely connected. The full list is in the related topics section of this page.
What should I study after Load Balancing in Parallel Programs?
Continue with False Sharing, the next topic in Concurrency & Parallelism.

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