Parallel computing is a type of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
Parallel computing is closely related to concurrent computing—they are frequently used together, and often conflated, though the two are distinct: it is possible to have parallelism without concurrency (such as bit-level parallelism), and concurrency without parallelism (such as multitasking by time-sharing on a single-core CPU). In parallel computing, a computational task is typically broken down in several, often many, very similar subtasks that can be processed independently and whose results are combined afterwards, upon completion. In contrast, in concurrent computing, the various processes often do not address related tasks; when they do, as is typical in distributed computing, the separate tasks may have a varied nature and often require some inter-process communication during execution.
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published: 19 Dec 2014
Introduction To Parallel Computing
Follow the MOOC at https://www.coursera.org/learn/parprog1
published: 30 Jan 2017
Introduction to Parallel Programming
References:
- The microprocessor data can be found here: https://github.com/karlrupp/microprocessor-trend-data/tree/master/42yrs
- Summary of parllel computing can be founnd here: https://medium.com/tebs-lab/the-age-of-parallel-computing-b3f4319c97b0
I suggest reading this if you're looking for a good guide on parallel programming.
- https://computing.llnl.gov/tutorials/parallel_comp/
- http://www.cac.cornell.edu/education/training/StampedeJune2013/ParallelProgramming.pdf
I've included some short examples of common parallel programming in this repo here.
https://github.com/JohnSongNow/youtube-videos/tree/master/parallel-programming
Scenes made with Godot 3.1. The tweening system and scenes for videos can be found here
https://github.com/JohnSongNow/youtube/tree/master/cutscene-library
...
published: 10 Aug 2019
Sequential and Parallel Computing
This video is an overview of the different computer processing operations - sequential computing and parallel, distributed computing.
published: 25 Aug 2020
AMD Simplified: Serial vs. Parallel Computing
So much is happening simultaneously in the realm of personal computing that staying abreast of the popular labels for the latest technology trends can be just as challenging as understanding the concepts and grasping the implications. The following video explains serial and parallel computing using a real life example to every day consumers.
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Cloud Computing
Introduction to Parallel Computing
main reasons
published: 14 Aug 2018
Why Parallel Computing
History and motivation behind parallel and distributed computing
published: 24 Aug 2020
MATLAB Parallel Computing
Round table: https://meetingsemea3.webex.com/meetingsemea3/j.php?MTID=m65a48efc8eb89b3768fd19da171a0f93
Learn how to speed up your code exploiting the potentialities of parallel computation in MATLAB and Simulink.
published: 26 May 2020
Introduction to Parallel Programming
Sign up for the class here:
http://www.udacity.com/course/cs344
Learn the fundamentals of parallel computing with the GPU and the CUDA programming environment! In this class, you'll learn about parallel programming by coding a series of image processing algorithms, such as you might find in Photoshop or Instagram. You'll be able to program and run your assignments on high-end GPUs, even if you don't own one yourself.
References:
- The microprocessor data can be found here: https://github.com/karlrupp/microprocessor-trend-data/tree/master/42yrs
- Summary of parllel computing ...
References:
- The microprocessor data can be found here: https://github.com/karlrupp/microprocessor-trend-data/tree/master/42yrs
- Summary of parllel computing can be founnd here: https://medium.com/tebs-lab/the-age-of-parallel-computing-b3f4319c97b0
I suggest reading this if you're looking for a good guide on parallel programming.
- https://computing.llnl.gov/tutorials/parallel_comp/
- http://www.cac.cornell.edu/education/training/StampedeJune2013/ParallelProgramming.pdf
I've included some short examples of common parallel programming in this repo here.
https://github.com/JohnSongNow/youtube-videos/tree/master/parallel-programming
Scenes made with Godot 3.1. The tweening system and scenes for videos can be found here
https://github.com/JohnSongNow/youtube/tree/master/cutscene-library
Discord: https://discord.gg/a7YcSeuH9H
Music:
Possimiste - "The Flight of Lulu" from the free music archive.
References:
- The microprocessor data can be found here: https://github.com/karlrupp/microprocessor-trend-data/tree/master/42yrs
- Summary of parllel computing can be founnd here: https://medium.com/tebs-lab/the-age-of-parallel-computing-b3f4319c97b0
I suggest reading this if you're looking for a good guide on parallel programming.
- https://computing.llnl.gov/tutorials/parallel_comp/
- http://www.cac.cornell.edu/education/training/StampedeJune2013/ParallelProgramming.pdf
I've included some short examples of common parallel programming in this repo here.
https://github.com/JohnSongNow/youtube-videos/tree/master/parallel-programming
Scenes made with Godot 3.1. The tweening system and scenes for videos can be found here
https://github.com/JohnSongNow/youtube/tree/master/cutscene-library
Discord: https://discord.gg/a7YcSeuH9H
Music:
Possimiste - "The Flight of Lulu" from the free music archive.
So much is happening simultaneously in the realm of personal computing that staying abreast of the popular labels for the latest technology trends can be just a...
Round table: https://meetingsemea3.webex.com/meetingsemea3/j.php?MTID=m65a48efc8eb89b3768fd19da171a0f93
Learn how to speed up your code exploiting the potentia...
Round table: https://meetingsemea3.webex.com/meetingsemea3/j.php?MTID=m65a48efc8eb89b3768fd19da171a0f93
Learn how to speed up your code exploiting the potentialities of parallel computation in MATLAB and Simulink.
Round table: https://meetingsemea3.webex.com/meetingsemea3/j.php?MTID=m65a48efc8eb89b3768fd19da171a0f93
Learn how to speed up your code exploiting the potentialities of parallel computation in MATLAB and Simulink.
Sign up for the class here:
http://www.udacity.com/course/cs344
Learn the fundamentals of parallel computing with the GPU and the CUDA programming environment!...
Sign up for the class here:
http://www.udacity.com/course/cs344
Learn the fundamentals of parallel computing with the GPU and the CUDA programming environment! In this class, you'll learn about parallel programming by coding a series of image processing algorithms, such as you might find in Photoshop or Instagram. You'll be able to program and run your assignments on high-end GPUs, even if you don't own one yourself.
Sign up for the class here:
http://www.udacity.com/course/cs344
Learn the fundamentals of parallel computing with the GPU and the CUDA programming environment! In this class, you'll learn about parallel programming by coding a series of image processing algorithms, such as you might find in Photoshop or Instagram. You'll be able to program and run your assignments on high-end GPUs, even if you don't own one yourself.
References:
- The microprocessor data can be found here: https://github.com/karlrupp/microprocessor-trend-data/tree/master/42yrs
- Summary of parllel computing can be founnd here: https://medium.com/tebs-lab/the-age-of-parallel-computing-b3f4319c97b0
I suggest reading this if you're looking for a good guide on parallel programming.
- https://computing.llnl.gov/tutorials/parallel_comp/
- http://www.cac.cornell.edu/education/training/StampedeJune2013/ParallelProgramming.pdf
I've included some short examples of common parallel programming in this repo here.
https://github.com/JohnSongNow/youtube-videos/tree/master/parallel-programming
Scenes made with Godot 3.1. The tweening system and scenes for videos can be found here
https://github.com/JohnSongNow/youtube/tree/master/cutscene-library
Discord: https://discord.gg/a7YcSeuH9H
Music:
Possimiste - "The Flight of Lulu" from the free music archive.
Round table: https://meetingsemea3.webex.com/meetingsemea3/j.php?MTID=m65a48efc8eb89b3768fd19da171a0f93
Learn how to speed up your code exploiting the potentialities of parallel computation in MATLAB and Simulink.
Sign up for the class here:
http://www.udacity.com/course/cs344
Learn the fundamentals of parallel computing with the GPU and the CUDA programming environment! In this class, you'll learn about parallel programming by coding a series of image processing algorithms, such as you might find in Photoshop or Instagram. You'll be able to program and run your assignments on high-end GPUs, even if you don't own one yourself.
Parallel computing is a type of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
Parallel computing is closely related to concurrent computing—they are frequently used together, and often conflated, though the two are distinct: it is possible to have parallelism without concurrency (such as bit-level parallelism), and concurrency without parallelism (such as multitasking by time-sharing on a single-core CPU). In parallel computing, a computational task is typically broken down in several, often many, very similar subtasks that can be processed independently and whose results are combined afterwards, upon completion. In contrast, in concurrent computing, the various processes often do not address related tasks; when they do, as is typical in distributed computing, the separate tasks may have a varied nature and often require some inter-process communication during execution.