greatbody
/
freeCodeCamp
mirror of https://github.com/freeCodeCamp/freeCodeCamp.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

added information about parallel computing (#19494) 

added about what is parallel computing, why parallelism, how to parallelize, data and task parallelism etc.
pull/19525/head
Adarsh Jain 2018-10-16 20:35:38 +05:30 committed by Quincy Larson
parent 5bc241a9a7
commit bbf63b3404
1 changed files with 77 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

81
client/src/pages/guide/english/computer-science/parallel-computing/index.md
View File

@ -3,11 +3,84 @@ title: Parallel Computing
---
## Parallel Computing
This is a stub. <a href='https://github.com/freecodecamp/guides/tree/master/src/pages/computer-science/databases/index.md' target='_blank' rel='nofollow'>Help our community expand it</a>.
In the simplest sense, parallel computing is the simultaneous use of **multiple compute resources** to solve a computational problem:
- A problem is broken into discrete parts that can be solved concurrently.
- Each part is further broken down to a series of instructions.
- Instructions from each part execute simultaneously on different processors.
- An overall control/coordination mechanism is employed.
<a href='https://github.com/freecodecamp/guides/blob/master/README.md' target='_blank' rel='nofollow'>This quick style guide will help ensure your pull request gets accepted</a>.
![Parallelism](https://computing.llnl.gov/tutorials/parallel_comp/images/parallelProblem.gif)
<!-- The article goes here, in GitHub-flavored Markdown. Feel free to add YouTube videos, images, and CodePen/JSBin embeds -->
### Why Parallelism
- Faster, of course
- Finish the work earlier - Same work in less time
- Do more work - More work in the same time
### How to parallelize
- Firstly, break down the computational part into small pieces.
- Then, sssign the small jobs to the parallel running processes.
- But it might become complicated when the small piece of jobs depend upon others.
### Writing a Parallel Program
- If you are starting with an existing serial program, debug the serial code completely.
- Identify which parts of the program can be executed concurrently.
- Requires a thorough understanding of the algorithm.
- Exploit any parallelism which may exist.
- May require restructuring of the program and/or algorithm. May require an entirely new algorithm.
- Decompose the program:
- Task Parallelism
- Data Parallelism
- Combination of both
### Task (Functional) Parallelism
- Different operations are performed on the same or different data in parallel to fully utilize the resources.
- Decomposing the problem into different processes which can be distributed to multiple processors for simultaneous execution.
- Good to use when there is not static structure or fixed determination of number of calculations to be performed.
### Data (Domain) Parallelism
- Same operations are performed on different subsets of same data structure.
- Partitioning the problem's data domain and distributing portions to multiple processors for simultaneous execution.
- Good to use for problems where:
- data is static.
- domain is fixed but computation within various regions of the domain is dynamic.
### Elements of a Parallel Computer
- Hardware
- Multiple Processors
- Multiple Memories
- Interconnection Network
- System Software
- Parallel Operating System
- Programming Constructs to Express/Orchestrate Concurrency
- Application Software
- Parallel Algorithms
### Communication Model of Parallel Platforms
- There are two primary forms of data exchange between parallel tasks
- accessing a shared data space and exchanging messages.
- Platforms that provide a shared data space are called **shared-address-space** machines or **mult iprocessors**.
- Platforms that support messaging are called **message passing platforms** or **multicomputers**.
### Shared-Address-Space Platforms
- Part (or all) of the memory is accessible to all processors.
- Processors interact by modifying data objects stored in this shared-address-space.
- If the time taken by a processor to access any memory word in the system (global or local) is
- identical, then the platform is classified as a **uniform memory access (UMA)**,
- not identical, then its classified as **non-uniform memory access (NUMA)** machine.
![Shared memory](https://computing.llnl.gov/tutorials/parallel_comp/images/sharedMemoryModel.gif)
### Message-Passing Platforms
- These platforms comprise of a set of processors and their own (exclusive) memory.
- Instances of such a view come naturally from clustered workstations and non-shared-addressspace multicomputers.
- Interactions between processes running on different nodes must be accomplished using messages.
- The exchange of messages is used to transfer data, work and to synchronize actions among the processes.
- These platforms are programmed using (variants of) **send** and **receive** primitives.
- Libraries such as **MPI** and **PVM** provide such primitives.
![Message passing](https://computing.llnl.gov/tutorials/parallel_comp/images/msg_pass_model.gif)
#### More Information:
<!-- Please add any articles you think might be helpful to read before writing the article -->
- [Parallel Computing on Wikipedia](https://en.wikipedia.org/wiki/Parallel_computing)
- [Introduction to Parallel Computing](https://computing.llnl.gov/tutorials/parallel_comp/)