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Advanced Topic 43 of 51 Concurrency & Parallelism

Parallel Graph Algorithms

Adapting traversals and shortest paths to many cores.

How it works

Parallel graph algorithms accelerate graph computations such as breadth-first search, shortest paths, connected components, PageRank, triangle counting and community detection on multicore, GPU and distributed systems. Typical approaches include level-synchronous BFS processing frontiers in parallel, label propagation and union-find for components, delta-stepping for single-source shortest paths and vertex-centric models such as Pregel and GraphX with supersteps. Graphs are stored as compressed sparse row formats.

Where it is used

Social-network analysis, web ranking, road routing, fraud detection, recommendation and bioinformatics use parallel graph processing, with frameworks such as Ligra, Gunrock and Giraph.

Common pitfall

Irregular memory access, poor locality and skewed degree distributions in power-law graphs cause load imbalance and low bandwidth use. Synchronization on shared vertex data needs atomics. Partitioning to minimize communication in distributed settings is hard. Choose representations, such as direction-optimizing BFS, handle high-degree vertices specially and measure traversed edges per second.

Where it fits in the course

Parallel Graph Algorithms is topic 43 of 51 in Concurrency & Parallelism, rated Advanced. It follows NUMA. The next topic is Concurrency Testing.

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 Parallel Graph Algorithms?
Adapting traversals and shortest paths to many cores. It belongs to Concurrency & Parallelism and is an advanced-level topic.
Where do people use Parallel Graph Algorithms?
Social-network analysis, web ranking, road routing, fraud detection, recommendation and bioinformatics use parallel graph processing, with frameworks such as Ligra, Gunrock and Giraph.
What is the most common mistake with Parallel Graph Algorithms?
Irregular memory access, poor locality and skewed degree distributions in power-law graphs cause load imbalance and low bandwidth use.
What should I learn before this topic?
Start with NUMA, 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. You can also explore Graph Traversal in the Visual Lab.
Is Parallel Graph Algorithms 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 Parallel Graph Algorithms?
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 Parallel Graph Algorithms?
NUMA, Concurrency Testing, False Sharing are closely connected. The full list is in the related topics section of this page.
What should I study after Parallel Graph Algorithms?
Continue with Concurrency Testing, the next topic in Concurrency & Parallelism.

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