Introduction to Parallel Processing

Active learning helps students grasp parallel processing because the concept is inherently physical and visual. Watching a sequential process unfold slowly and then seeing the same work completed faster in parallel makes the efficiency gains tangible. This hands-on approach corrects the misconception that parallel processing is always faster by letting students experience the overhead of setup and merging firsthand.

• 10th Grade
• Computer Science
• 3 activities
• 20 min–30 min

Learning Objectives

1. Compare the execution time of sequential versus parallel algorithms for a given task.
2. Analyze simple problems to identify tasks that are suitable for parallel execution.
3. Explain the relationship between the number of processors and potential performance gains.
4. Evaluate the trade-offs between complexity and performance when designing parallel solutions.

Simulation Game

⏱ 25 min·Whole Class

Simulation Game: Sequential vs. Parallel Sort Race

One student sorts a deck of 50 numbered cards in order while a group of five students each sort a subset of 10 cards simultaneously. The class records time for both approaches, discusses the actual speedup ratio, and identifies the overhead costs (dividing the deck, merging results) that reduce the theoretical maximum speedup.

Prepare & details

Explain the basic idea of parallel processing.

Facilitation Tip: During the Sequential vs. Parallel Sort Race, prepare identical sets of cards so students compare times fairly and focus on the merge step’s impact.

Setup: Flexible space for group stations

Materials: Role cards with goals/resources, Game currency or tokens, Round tracker

• Apply
• Analyze
• Evaluate
• Create
• Social Awareness
• Decision-Making

Think-Pair-Share

⏱ 20 min·Pairs

Think-Pair-Share: Can This Be Parallelized?

Give students a list of six tasks: rendering each frame of a video independently, processing steps of a sequential recipe, searching separate sections of a database, computing a running total where each value depends on the previous, resizing 1,000 images, and building floors of a skyscraper. Students individually classify each as parallel-friendly or sequentially constrained, then compare reasoning with a partner.

Prepare & details

Analyze simple problems that can benefit from parallel execution.

Facilitation Tip: For the Can This Be Parallelized? activity, provide two sample code snippets—one with clear dependencies and one without—so students practice identifying bottlenecks.

Setup: Standard classroom seating; students turn to a neighbor

Materials: Discussion prompt (projected or printed), Optional: recording sheet for pairs

• Understand
• Apply
• Analyze
• Self-Awareness
• Relationship Skills

Inquiry Circle

⏱ 30 min·Small Groups

Inquiry Circle: Amdahl's Law in Practice

Groups receive a scenario where 60% of a program can be parallelized and must calculate the theoretical maximum speedup for 2, 4, 8, and unlimited processors. They graph the results, identify the diminishing returns, and write a one-paragraph recommendation explaining when adding more processors stops being cost-effective.

Prepare & details

Compare the challenges of sequential versus parallel task execution.

Facilitation Tip: When investigating Amdahl's Law in practice, ensure students calculate sequential fractions from real data before extrapolating to processor counts.

Setup: Groups at tables with access to source materials

Materials: Source material collection, Inquiry cycle worksheet, Question generation protocol, Findings presentation template

• Analyze
• Evaluate
• Create
• Self-Management
• Self-Awareness

Teaching This Topic

Start with the physical card sort to build intuition about parallelism before introducing theory. Emphasize Amdahl's Law as a thinking tool, not just a formula, to help students reason about real-world constraints. Avoid diving deep into thread synchronization details; focus on decomposition and speedup concepts. Research suggests students learn parallelism best when they first see its limits through concrete examples before abstracting to hardware or software models.

What to Expect

Students will explain why parallel processing is not universally faster and identify sequential bottlenecks in a task. They will use Amdahl's Law to predict speedup and justify task decomposition choices based on processor count. Clear evidence of learning includes accurate identification of parallelizable and sequential portions in short code or task scenarios.

Watch Out for These Misconceptions

Common Misconception

During the Sequential vs. Parallel Sort Race, watch for students who assume the parallel version will always finish faster.

What to Teach Instead

After the race, have groups compare their total times including setup and merge. Ask them to explain why a small task with many merges might still lose to a simple sequential sort.

Common Misconception

During the Think-Pair-Share activity, watch for students who believe any task can be split given enough processors.

What to Teach Instead

Use the provided code snippets in this activity to redirect students: highlight the sequential portion in the dependent task and ask them to explain why it cannot be parallelized further.

Common Misconception

During the Collaborative Investigation of Amdahl’s Law in Practice, watch for students who think adding more cores always improves performance.

What to Teach Instead

Have students plug their sequential fraction into the Amdahl calculator and test doubling cores from 4 to 8. Ask them to articulate why the speedup curve flattens as cores increase.

Assessment Ideas

Exit Ticket

After the Think-Pair-Share activity, provide students with two short code snippets. Ask them to identify which is embarrassingly parallel and explain their reasoning in 2-3 sentences.

Quick Check

During the Sequential vs. Parallel Sort Race, circulate and ask each group to estimate their merge time before starting. After the race, compare their estimate to the actual time to assess their understanding of overhead.

Discussion Prompt

After the Collaborative Investigation of Amdahl’s Law in Practice, facilitate a whole-class discussion using the prompt: ‘If a task is 20% sequential, what is the maximum speedup possible with 10 processors? What if processors double to 100?’

Extensions & Scaffolding

• Challenge: Ask students to design a parallel algorithm for a task with moderate dependencies (e.g., counting word frequencies) and calculate its theoretical speedup.
• Scaffolding: Provide a partially completed Amdahl’s Law calculator spreadsheet with prompts to fill in sequential and parallel portions.
• Deeper exploration: Have students research GPU vs. CPU parallelism and present a one-slide comparison of where each excels.

Key Vocabulary

Parallel Processing	A method of computation where multiple processors or cores work simultaneously on different parts of a single task to speed up execution.
Sequential Processing	A method of computation where tasks are executed one after another in a specific order, with each task completing before the next begins.
Task Decomposition	The process of breaking down a large, complex problem into smaller, independent subtasks that can be processed individually.
Amdahl's Law	A formula that calculates the theoretical speedup in latency of the execution of a task at fixed workload that can be expected of a system whose resources are improved.
Embarrassingly Parallel	A type of task that can be easily divided into many independent subtasks with little or no communication needed between them, making it ideal for parallel processing.