Index
Introduction to Parallel Processing and Computing¶
Basic Definition of Parallel Computing¶
Parallel computing simply means doing multiple computing things at the exact same time (āĻāĻāĻ¯ā§āĻā§). Instead of executing one task after another, we run many calculations simultaneously.
We use this model to solve very large problems. If a problem is too big for a single computer to handle in a short time, we use parallel computing to finish it quickly.
Serial Computing vs. Parallel Computing¶
1. Serial Computing Model¶
Traditionally, standard computer software is written for serial computation. It works on the following principles:
- Single Central Processing Unit (CPU): The software runs on a single computer that has only one processor core.
- Sequential Steps: A big problem is broken down into a series of different instructions.
- One by One Execution: Instructions are executed sequentially (āĻ§āĻžāĻĒ āĻ āĻ¨ā§āĻ¸āĻžāĻ°ā§), meaning one instruction runs after another finishes.
- Time Limitation: At any single moment in time, only one instruction can be executed by the processor.
2. Parallel Computing Model¶
Parallel computing is the simultaneous use of multiple computational resources to solve a problem. It works like this:
- Multiple CPUs: It requires a computer system that has more than one processor or CPU.
- Problem Division: A large problem is divided into discrete (āĻĒā§āĻĨāĻ) smaller parts.
- Simultaneous Processing: Each smaller part is broken into instructions, and these instructions execute concurrently (āĻ¸āĻŽāĻžāĻ¨ā§āĻ¤āĻ°āĻžāĻ˛āĻāĻžāĻŦā§) on different processors.
Computing Resources and Problem Types¶
What are Parallel Computing Resources?¶
To run parallel computing, we need specific hardware resources:
- A single computer that contains multiple processors or cores inside it.
- A collection of multiple computers connected together using a computer network.
- A hybrid combination of both multiple processors inside one machine and a network of computers.
Characteristics of a Parallel Problem¶
We cannot solve every computer problem using parallel processing. A problem must have these specific characteristics:
- The problem must be breakable into discrete pieces of work that can be solved at the same time.
- Multiple program instructions must execute at any moment without causing errors or data mixing.
- Solving the problem with multiple computing resources must take less time compared to using a single resource.
Real-World Examples and Analogy¶
The Ticket Counter Analogy¶
Think about a regular life example. If you go to a bank or a theater to get a ticket, and there is only one cashier window, everyone stands in a single line (āĻ˛āĻžāĻāĻ¨). The cashier serves people one by one. This is serial processing. If the line becomes very long, the cashier faces a lot of difficulty, and it takes too much time.
But if we open two or three counters with separate cashiers, multiple people can get tickets at the same time. This is parallel processing. It decreases the waiting time.
The Real World is Parallel¶
Our physical universe works in a parallel way. In nature, many complex things happen simultaneously. For example:
- Planetary movements and galaxies are moving at the exact same time.
- The human brain functions by processing multiple signals together.
- Weather changing, atmospheric modeling, and city traffic flows happen concurrently.
Because our real world is parallel and very complex, serial computers cannot simulate or model these processes correctly. We need parallel computing for accurate simulations (āĻāĻŖāĻ¨āĻžāĻŽā§āĻ˛āĻ āĻ¸āĻŋāĻŽā§āĻ˛ā§āĻļāĻ¨).
Why Do We Need Parallel Computing?¶
There are several major reasons why parallel computing is necessary today:
- Saving Time and Money: Using multiple resources at the same time saves a lot of processing time. Saving time directly decreases the operational cost (āĻāĻ°āĻ).
- Solving Large Problems: Think about modern web search engines. They process millions of information requests per second. A single processor cannot handle this massive scale of data.
- Using Non-Local Resources: We can connect different computers over a network. Parallel computing allows one system to use the data and memory of another remote computer easily.
- Providing Concurrency: It allows the system to do many different things at the exact same chronological instance (āĻāĻāĻ āĻ¸āĻŽāĻ¯āĻŧā§).
- Limits of Single Processors: Physical processors have reached a speed limit. We cannot make a single CPU core any faster because of high heat dissipation (āĻ¤āĻžāĻĒ āĻ¨āĻŋāĻ°ā§āĻāĻŽāĻ¨) and power consumption. Therefore, adding more processors in parallel is the only way to increase speed.
Common Practical Applications¶
Parallel computing is highly used in the following areas:
- Scientific Computing: For nuclear simulations, chemical molecular modeling, and complex math calculations.
- Business and Databases: For running web search engines, online transaction processing (OLTP), data mining, and big management information systems.
- Geographic Information Systems (GIS): For handling Google Maps data, seismic data analysis, and earth mapping.
- Artificial Intelligence (AI): For running machine learning, deep learning, and multi-layered neural networks (āĻ¨āĻŋāĻāĻ°āĻžāĻ˛ āĻ¨ā§āĻāĻāĻ¯āĻŧāĻžāĻ°ā§āĻ).
- Real-Time Control Systems: For managing robotic hardware, automatic machinery, and high-speed speech processing.