The Different Ways to Process Parallel Data Streams

Technology has changed the way that we do business. One of the most valuable components of this technological revolution is how quickly information is computed over operating systems. This is often done through the method of processing data through parallel means.

There are many ways that such information can be distributed through these parallel methods. We’ll focus on a few of those ways today.

Massively Parallel Process (MPP)


One of the ways that you can process parallel data streams first includes utilizing the method of the massively parallel process (MPP). Through this type of parallel process, you’re using a processing paradigm where hundreds or even thousands of processing nodes work parallel in parts of a computational task. Each of the nodes located within this CPU parallel processing method runs individual instances of an operating system. These components have their own input and output devices, and they don’t share memory.

This form of parallel processing is researched by such companies as data software industry leader TIBCO in the hopes of creating high-performance data transmission. There are organizations that use the MPP method to deal with high-volume of ever-expanding data use. The goal is to maintain the high performance for their CPUs, while still performing the scientific computing needed to maintain these parallel data streams. One such example of a company that does this includes insurance companies.

These businesses deal with millions of customers each year, providing quality insurance coverage. As customer numbers increase, so does the amount of data that the insurance company has to deal with. MPP helps to deal with this larger amount of data that comes through the companies’ databases. Even with this being the case though, in some circumstances, there sometimes might be a delay with processing the customer’s data. This form of processing data is one of the different ways in that you can process parallel data streams.

Symmetric Multi-Processing (SMP)

Another way that you can process parallel data streams is through using symmetric multi-processing (SMP). This high-performance form of parallel processing involves a single system, that has multiple, and tightly coupled processors. These different processors share the operating system, I/O devices, and memory. This form of shared memory processing creates efficient processors. This can help a company that is seeking a way to process parallel data streams quickly and efficiently.

Some of the characteristics of this form or processing include the fact that all the processors are treated identically. This avoids the occurrence of many parallel problems from taking place. Secondly, there is a shared memory that allows for communication amongst the processors. Lastly, the symmetric multi-processing method is complex in design since all the units share the same memory and data buses.

The symmetric multi-processing method can be used in a variety of situations, no matter the type of business you’re running. For example, imagine you’re running a staffing agency. One of the things that you’ll have to deal with is endless data regarding potential candidates for work assignments. By using a symmetric multi-processing method you have your way to process parallel data streams. This can provide you with instantaneous access to client information, in addition to the information you’ll need on your applicants. With the symmetric multi-processing method, you can sift through this data, completing different tasks all at once. In recent years, as data dissemination continues to grow, the type of parallel process has become an invaluable tool.

Pros and Cons


It’s best to weigh your options when it comes to MPP vs. SMP. Each brings in tow its own plusses and minuses. In the case of the symmetric multi-processing method, these systems are often the cheaper parallel process. The symmetric multi-processing system though can be limited in how much it can scale. For MPP, these systems can grow infinitely. They also tend to be a bit pricey. Do your homework to see that form of parallel processing works best for you.