Parallel Computing

Systems And Networks
strategy

Also known as: parallel processing

Grade 9-12

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Parallel computing is the practice of dividing work so multiple processors, cores, or computers can perform parts of the computation at the same time. Modern computing relies on parallelism in phones, laptops, game systems, supercomputers, and cloud services.

Definition

Parallel computing is the practice of dividing work so multiple processors, cores, or computers can perform parts of the computation at the same time. It is useful when one large task can be separated into smaller tasks that can run together.

💡 Intuition

Instead of one person doing every part of a job in order, several people work on different pieces at the same time.

🎯 Core Idea

Parallelism can reduce running time, but only when the work can actually be split and coordinated well.

Example

A graphics card can process many pixels in parallel when rendering an image, and a data center can split a large computation across many machines.

Formula

\text{speedup} = \frac{T_1}{T_p}

🌟 Why It Matters

Modern computing relies on parallelism in phones, laptops, game systems, supercomputers, and cloud services. Students increasingly meet it in AI, simulations, and graphics.

💭 Hint When Stuck

When checking whether a task can run in parallel, look for parts that do not depend on each other. Then compare the extra coordination cost against the time saved.

Formal View

Parallel computing distributes work across multiple processing units. Performance is often described by speedup T_1/T_p, comparing one processor to p processors.

🚧 Common Stuck Point

Not every problem parallelizes well. Some steps still have to happen in sequence.

⚠️ Common Mistakes

  • Assuming more processors always produce proportional speedup
  • Ignoring the overhead of coordination and communication
  • Trying to parallelize steps that depend heavily on each other

Frequently Asked Questions

What is Parallel Computing in CS Thinking?

Parallel computing is the practice of dividing work so multiple processors, cores, or computers can perform parts of the computation at the same time. It is useful when one large task can be separated into smaller tasks that can run together.

What is the Parallel Computing formula?

\text{speedup} = \frac{T_1}{T_p}

When do you use Parallel Computing?

When checking whether a task can run in parallel, look for parts that do not depend on each other. Then compare the extra coordination cost against the time saved.

How Parallel Computing Connects to Other Ideas

To understand parallel computing, you should first be comfortable with computing system and algorithm. Once you have a solid grasp of parallel computing, you can move on to artificial intelligence.

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