What Is Parallel Processing?
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Serial processing is the processing of information using multiple computer processors simultaneously to work through a problem. Computers that are designed for serial processing are called parallel processors or parallel computers. The more general term for parallel processors is supercomputers. Exactly what is parallel processing? Massively parallel processing (MPP) is a processing paradigm in which processing nodes work on different processes of a computational task in parallel. Each node runs individual instances of an operating system and achieves a common computational task by communicating over a high-speed connection. Organizations that deal with large amounts of different data rely on parallel processing.
Understanding the Hardware Components
There are several hardware components of massively parallel computer architecture. Processing nodes are the basic building blocks of parallel programs. They are homogenous processing cores with one or more central processing units. Task parallelism requires a high-speed interconnect so that nodes can communicate while performing different tasks. The use of parallel processing requires low latency, high bandwidth connection. A distributed lock manager (DLM) coordinates the sharing of external memory or disk space among nodes. It receives requests for resources from nodes and connects them when the resources are available. A DLM ensures consistent data quality and recovery of failed nodes. A parallel processing factor environment belongs to one of two groups depending on how the nodes share resources, shared disk systems, and shared-nothing systems.
The Pros and Cons of Shared Disk Systems
Each processing node in a shared disk system has one or more central processing units (CPUs) and an independent random-access memory (RAM). These nodes share an external disk space for file storage and are connected by a high-speed bus. The scalability of a shared disk system depends on the bandwidth connection and the hardware constraints on the DLM. The parallel processing system becomes readily available as the nodes share an external database. Data will never be lost if a node is damaged, and it’s easy to add new nodes to the system. The disadvantage of a shared disk system is that coordinating the data access is complicated. The system requires a reliable distributed lock manager and sufficient bandwidth. This requires an operating system to control the shared disk, which adds additional overhead costs.
The Pros and Cons of Shared Nothing
Another popular parallel computer architecture is the shared-nothing architecture. Each processing node has independent random access memories and disks that store files and sets of data. The data to be processed is shared among the nodes using different techniques. Shared-nothing systems are horizontally scalable to include a large number of nodes. Adding a new node is easy since they function independently. A shared-nothing system is a good option for a read-only database. When one node fails, the others remain unaffected, resulting in minimal chances of database corruption.
Utilizing a shared-nothing system with distributed databases requires a lot of coordination to complete a task. Each node owns a slice of the database, which makes data management difficult. Shared nothing systems aren’t suitable for parallel applications with a vast amount of input data requirements. A computation that requires a lot of data modification operations also isn’t suited for a shared-nothing architecture.
There are several advantages of parallel computing. Parallel programming helps save organizations time and money by sorting through large data sets more efficiently. The more efficiently resources are used to solve a particular problem, the more money saved. Parallel programming is a great way to solve more complex problems or larger problems. There are several examples of parallel processing in motion daily such as dual-core processors in smartphones, the Intel processors of computers, blockchain technology, multithreading, and the Internet of Things (IoT).
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