Parallelism

As explained in Introduction to Generative AI, Second Edition

Parallelization is the execution of independent subtasks or tool calls concurrently rather than sequentially.

Parallelization runs independent subtasks or tool calls at the same time instead of waiting for each sequentially. In agent architectures, planning can expose these opportunities and reduce delays on complex, multistep work. It cannot replace dependencies: when one call needs another’s result, those actions must remain ordered. section 8.12

A website with recent pickleball news

An agent can generate a website’s basic frontend while researching documentation for a news API. After selecting and calling the API, it can populate the site with retrieved stories and complete dependent details. section 8.12

Figure 8.8 A sample execution flow for a task with planning and parallelization

The execution-flow diagram shows how planning divides a larger task, runs independent actions together, and sequences dependent follow-up work. section 8.12

How Introduction to Generative AI, Second Edition covers it

How deep it goes

Explains the principle and task dependencies through a workflow example, but does not detail implementation mechanics.

Examples

Uses a pickleball news website whose frontend work and API research proceed in parallel.

What it assumes

Assumes familiarity with multistep agent tasks and tool calls while clarifying independence and dependency from examples.

What you can do after

Apply it by separating independent subtasks from actions whose inputs depend on earlier results.

How it connects

Connects parallelization with agent architectures, planning, tool calls, reflection, and code generation.

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Related concepts

• concurrencycontrasts with · Concurrency is the structuring or execution of multiple computational activities so their progress overlaps in time, whether or not they run simultaneously.
• sequential processingcontrasts with · The execution of operations one after another in a defined order, without overlapping their progress.
• diminishing returnsproduces · Diminishing returns is a pattern in which successive increases in an input produce progressively smaller incremental gains in an outcome.
• threaduses · A thread is an independently schedulable sequence or context of execution within a program or process.
• data parallelincludes · Data parallelism is a parallel-computing technique that applies the same computation concurrently to separate portions of data.

This explainer was generated by Manning's knowledge graph from the text of Introduction to Generative AI, Second Edition. The section links point to the parts of the book it draws on.