What is Parallel Processing?

It’s more important than ever for businesses to have trust in their data and their processing systems to guide them through their business decisions. Investing in greater computing can make the difference between a bogged-down supply chain to success over a competitor. Let’s take a look at how something as simple as the right processor can be the difference in forwarding a company towards its ultimate goals.

Understanding Processing

One key to success in dealing with data values or other inner workings of technology is to have parallel applications acting in accordance with each other. Massively parallel processing, or MPP, is a processing paradigm where a distinct amount of processing nodes work on parts of a computational task parallel to one another. Each of these nodes run in individual instances for an operating system. Each has its own input and output devices, never sharing memory, but achieving a common task by communicating with each other through a high-speed interconnect.

Organizations that handle a large data stream rely on MPP for their data processing. As the number of customers increases for a company, so does its data. This is a loosely coupled system where nodes don’t share disk space, essentially an array of independent processing nodes communicating over a high-speed interconnection bus. This differs from symmetric multiprocessing, which includes multiple, tightly coupled processors. These processors share the operating system, devices, and memory. While that is a cheaper option to MPP, it is also significantly limited on how much it can scale.

Components of Processing


When dealing with a set of data, you want to make sure that your structures are up for any specific task. It’s essential to understand the hardware components of your processing system. Processing nodes are the basic building blocks of automatic parallelization. These nodes are simple processing cores with one or more central processing units, or CPUs visualized through a desktop computer. Within a processing system, these need to regularly communicate with each other to solve a particular problem. High-speed interconnect is crucial to system performance so a low latency, high bandwidth connection is an absolute necessity.

In those MPP architectures where the external memory is shared among the nodes, a distributed lock manager, or DLM, coordinates resource sharing. The DLM takes a request for resources and connects them when the resources are available. For efficient processors, the distributed lock manager ensures data consistency and recovery of any failures in the processor frequency. Whether it’s a handful or a large number of processors, MPP architectures belong to two major groups depending upon how resources are shared.

Shared Disk Systems vs. “Shared Nothing” Systems


As mentioned, there are two core groups that MPP architecture falls under: shared disk systems and “shared nothing” systems. Each processing node within shared disk systems has one or more CPUs and an independent random-access memory, or RAM. Those are shared external disk spaces for the storage of files. These are then connected with a high-speed bus. The scalability of the shared disk systems depends on the bandwidth of the high-speed interconnect and the hardware constraints on a distributed lock manager.

A “shared nothing” system is a more popular architecture. The processing nodes have independent RAM and disks that store necessary data values and files. The data structures that need to be processed are shared among them using various techniques. One of these is a replicated database. In this method, each processing a complete copy of the data, reducing the risk of losing information. There’s also the distributed database structure, where larger problems are partitioned into multiple slices, with each assigned a particular task to avoid redundancy. It’s all in the name of having a computer network that does the job for your company in the long run.