Parallel Processing is an area of study in cluster computing. It is also very interesting and important subject. In this article, I will explain to you the essentials you need to know about the processing parallel in computers. Furthermore, we will discuss why we use this processing method. And, theories to know about this, architectures too. Finally, I will describe the embarrassingly parallel problem with you
If you are interested to know more about this processing method keep reading this article. If you would like to know more basic about cluster computing. You can read my previous article too. Keep scrolling with me.
What is Parallel Processing?
The word Parallel Processing describes the meaning itself. It is processing resources simultaneously and uses them to solve problems. All the resources should use together. Mainly, a certain problem is broken into parts which are discrete. And those parts are solved concurrently. The instructions to solve each individual part is executed parallel in different CPUs.
Why use Parallel Processing?
There are a few reasons to use this processing method in cluster computers. Let us talk about each reason in details.
- Parallel Processing saves time
Solving problems concurrently at the same time will reduce the time when it is done in sequentially. So it will save time and at that time we can use to solve other problems
- To solve other problems which are large and complex
These Parallel Processing method can use to solve larger problems. Or complex problems. Because we can use more resources at the same time. And most of these large and complex problems are harder to solve using a single computer. Because of that, we can use cluster computing and process parallel.
- Can use Non Local resources
In a local area network, the resources connected is not enough sometimes. And some resources are scarce. So we can use other resources when we connect to a wide area network such as the internet. And use this parallel method for processing easily.
- Serial Computing has limits
When the Processing is serial and sequential there are limits. There is a limit on how fast the data can move through hardware. The power consumption is high. Therefore, there can be heating issues. Economic is also an issue. Making a single processor work faster is really expensive. We can use multiple execution units, pipelined instructions and multi core to make parallelism work.
Parallelism in Hardware
- Multiple Usage of Execution units.
Operations and calculations performed in the execution unit in the cp. If we can use multiple units, the solving process is parallel and can solve more problems too.
- Pipelining the instructions
We can use the instruction pipeline method. So that we can allow the CPU to have a faster throughput. It means we can send the number of instructions to execute in a unit of time.
- Usage of Multi Core
Cores are identified as independent processing units. A single computer can actually work with the multi core to increase the performance of the device.
The instruction Cycle
It is necessary for you to be aware of the instruction cycle when discussing parallelism. Here is the image below.
The first task is to fetch the instruction from the pipeline. Then the instruction decodes in the decode unit. The instruction will decode and send it to the fetch operand. Here the instruction is divided into two parts. First, the operand address calculation. Then the operand is fetching inside it. Next, the instruction execution in the next block. There, instruction executes and the operand write back is done.
Parallel Processing Architectures
There are a few parallel architectures we will study in this chapter.
- Shared Memory: Parallel Processing shared memory architecture makes all processors access the memory as globally. All memory can assess as a global address space. If there is a change in the location of the cell, all the processors can see the change
These shared memory architecture machines are divided into 2 main categories. It is based upon the time for memory access.
- Uniform Memory Access
- Non-Uniform Memory Access
2. Distributed Memory Architecture in Parallel Processing: In this, the processes have their own local memory. If there is a change in memory. The other processors are not affected. Therefore, it is also not visible to others. But, for this, there is a requirement for explicit programming.
3. Hybrid Distributed- Shared Memory: This is a combination of both shared architecture and distributed architecture in of cluster computing. The shared component has a cache coherent SPM machines. And the distributed component works as networking of multiple SMPs.
Embarrassingly Parallel Processing Problem
To start with, this Embarrassingly parallel problem exists in cluster computing. You might wonder what it is. As a matter of fact, there is little or no effort needed to separate a problem. The separation needs to use as parallel tasks. So there is no communication between the divided tasks. Furthermore, no dependency too.
As an example, the rendering of computer graphics is a Parallel Processing task. If you are creating a distributed relational database queries using distributed sets. It is also a task of processing parallel.
Applications of Parallel Processing
There are so many applications of Parallel Processing in the industry. Aerospace, Automobile, Biology software, energy, medicine, defense and so much more. Most usages are on the research level. logistic and financial services have the second highest usage in the industry.
To conclude, in this article we learned about Parallel Processing. What is parallelism. Why should we need to use this method? We learned about the instruction cycle. It is very important to know when we are leaning this processing method in cluster computing. The architectures including shared memory, distributed memory, and hybrid memory architecture.And also we learned about the embarrassingly parallel problem. Which means it is very easy to solve something using this processing method. Furthermore, we discussed the applications of the parallelism.
I hope you gained good knowledge of Parallel Processing in cluster computing. You can read my previous article to read more about the basics of cluster computing.