Parallel and Distributed Computing
David Foster
8 min read
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Study Guide Overview
This study guide covers parallel and distributed computing, focusing on the differences between sequential, parallel, and distributed approaches. It explains how to calculate execution time and speedup for parallel solutions, emphasizing limitations like sequential portions and diminishing returns. The guide includes example problems and practice questions covering these concepts, preparing students for multiple-choice and free-response exam questions.
#Parallel and Distributed Computing: Your Ultimate Study Guide 🚀
Hey there, future AP Computer Science Principles master! Let's break down parallel and distributed computing, making sure you're totally prepped for anything the exam throws your way. We're going to make this stick, and you'll be feeling confident in no time!
#Sequential vs. Parallel vs. Distributed Computing
#Sequential Computing
- Definition: Instructions are processed one after another, like a single line at a grocery store. 🚶
- Limitation: Can't keep up with the demand for faster processing; single processors can only be so fast before they overheat. 🔥
#Parallel Computing
- Definition: A program is broken into smaller operations, some of which are done simultaneously using multiple processors (cores). Think of it like having multiple checkout lines at the grocery store. 🛒🛒
- Advantages:
- Saves time and money by performing tasks concurrently.
- Scales more effectively than sequential solutions, handling increased workloads better.
- Real-World Example: Most modern computers with multi-core processors. 💻
Remember, parallel computing uses multiple processors within the same machine to speed up processing. This is different from distributed computing.
#Distributed Computing
- Definition: Multiple devices (computers) work together to run a program, often communicating over a network. Imagine different branches of the same grocery store working together to fulfill a large order. 🌐
- Advantages:
- Solves problems that are too big for a single computer due to storage or processing limitations.
- Harnesses the power of multiple devices working together.
- Real-World Examples: Search engines, cloud storage (Gmail, Google Docs), and cryptocurrency mining. ☁️
Parallel computing uses multiple processors in one machine, while distributed computing uses multiple machines. Keep this distinction clear!
#Execution Time, Efficiency, and Speedup Calculations
The AP exam will test your ability to calculate execution times, compare efficiencies, and determine speedups. Let's get...
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