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Massive Parallelism

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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM


The term "massively parallel" refers to the use of a huge number of computer processors (or independent computers) to perform a set of coordinated computations simultaneously. GPUs have tens of thousands of threads and are massively parallel architectures.

MPP (massively parallel processing) is a storage structure that allows numerous processors to conduct program activities in a coordinated manner. Each CPU has its operating system and memory and can work on various program parts. MPP databases can now manage vast volumes of data and do considerably faster analytics on large datasets.

Massively parallel processing is used by organizations that deal with large volumes of ever-increasing data. Consider a well-known insurance company with millions of customers. Customer data grows in tandem with the number of customers. Even if the company uses parallel processing, client data may take longer to process. Assume a Data Analyst executes a query against a database with 100 million rows. If the company adopts a 1000-node massively parallel processing system, each node is only responsible for 1/1000 of the total computing load.

MPP Database architectures come in a variety of shapes and sizes, each with its own set of advantages:

Grid computing is one technique in which the processing capacity of numerous computers in the spread, heterogeneous administrative domains is employed as needed whenever a computer is available. BOINC, for example, is a volunteer-based, opportunistic grid system in which the grid only provides power depending on the best effort.

Another method is to bring numerous processors nearby, as in a computer cluster. The speed and flexibility of the network become extremely crucial in such a centralized system, and modern supercomputers have adopted a variety of technologies, ranging from upgraded InfiniBand systems to three-dimensional torus interconnects.

Massively parallel processor arrays (MPPAs), a form of an integrated circuit containing hundreds or thousands of central processing units (CPUs) and random-access memory (RAM) banks, are also referred to as massively parallel processors arrays (MPPAs). These processors communicate with one another via a reconfigurable network of channels. An MPPA chip may perform more demanding tasks than traditional chips because it uses several processors running in parallel. MPPAs is built on a software parallel programming approach to design high-performance embedded system applications.

Advantages of Massive Parallelism

  • They are increasing the number of employees in an organization who can execute their own data analysis and queries at the same time without incurring lag or lengthier response times.
  • Having all of your info in one place.
  • They are making it easier to find insights and create dashboards with more relevant data than those produced from fragmented data.

Recommended read:- Cloud Computing

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Major Hardware Components of Massive Parallelism

To understand diverse architectures, you must first understand the physical components of a massively parallel processing system.

Processing Nodes

Massively parallel processing is built on the foundation of processing nodes. Simple, homogenous processing cores with one or more central processing units make up these nodes. The nodes can be compared to standard desktop computers.

High-Speed Interconnection

A massively parallel processing system's nodes operate on different aspects of the same calculation issue in parallel. Even though their processing is independent of one another, they must frequently communicate to solve a common problem. Between the nodes, a low-latency, high-bandwidth link is required. This is referred to as a bus or a high-speed connector. An ethernet connection, a fiber distributed data interface, or any other proprietary connection mechanism could be used.

Distributed Lock Manager (DLM)

A distributed lock manager (DLM) controls resource sharing in parallel processing architectures where external memory or disc space is shared among the nodes. When resources are available, the distributed lock management accepts requests from several nodes and connects them. The distributed lock manager enables data consistency and recovery of any failed node in various systems.

Architecture Of Massive Parallelism

Depending on how the nodes share their resources, there are two types of massively parallel processing systems.

Shared Disk Systems

Each processing node will have one or more central processing units (CPUs) and its random-access memory (RAM) in the shared disc system. On the other hand, these nodes share an external drive for file storage. A high-speed bus links these processing nodes together. The bandwidth of the high-speed link and the hardware limits on the distributed lock management determine the scalability of shared disc systems.


The parallel processing system becomes extremely accessible since all nodes share a single external database. Even if one node is broken, no data is permanently lost. Because they do not require the usage of a distributed database, shared disc systems are simpler. In shared storage systems, adding new nodes is simple.


The coordination of data access is complicated since the processing nodes share a single disc. The system requires distributed lock management. These node-to-node connections consume a portion of the high-speed interconnect's bandwidth. An operating system is necessary to manage the shared drive. This adds to the workload.

Shared Nothing Systems

The "shared nothing" design is a more popular architecture for massively parallel processing systems. The processing nodes have their random-access memory and disc where the relevant files and databases are stored. Various strategies exchange the data that needs to be processed across the nodes.

Replicated Database: Each processing node possesses a complete copy of the data in a replicated database. Even if a few nodes fail, the risk of data loss is negligible in this model. This model comes with the added benefit of more storage capacity.

Distributed Database: The database is partitioned into numerous slices in this manner. Each processing node owns and works on a certain portion of the database. Because there is no redundancy in this method, it saves a lot of disc space. This solution, however, is more complicated than using a replicated database. A large amount of data is moved between the nodes to complete the operation. The interconnect bus will be busier as a result of this. Data loss is a possibility because there is no redundancy in this paradigm.


Shared nothing systems have the ability to scale horizontally to encompass a large number of nodes. Adding a new processing node is easier because the processing nodes are relatively independent. The "shared nothing" solutions operate well if the database is read-only. The failure of one node has no impact on the other nodes because they are nearly self-contained. In "shared nothing" systems, the risks of database corruption are quite low.


To achieve a common job, "shared nothing" systems with dispersed databases require a lot of collaboration. Each node owns slices of the database. It may be challenging to manage this database. Shared nothing systems with a replicated database are unsuitable for applications that demand a lot of data. The "shared nothing" design may not be viable if the calculation requires a lot of data modification operations like data insertion and join.

Frequently Asked Questions   

1. What is massively parallel processing, and how does it work?
MPP (massively parallel processing) is the simultaneous execution of a program by several processors working on distinct program sections, each having its operating system and memory. MPP processors usually communicate via a messaging interface.

2. What is a massively parallel system, and how does it work?
The term "massively parallel" refers to the use of a huge number of computer processors (or independent computers) to perform a set of coordinated computations simultaneously. GPUs have tens of thousands of threads and are massively parallel architectures.

3. What are some of the benefits of massively parallel processing?
Each CPU has its operating system and memory and can work on various program parts. MPP databases can now manage vast volumes of data and do considerably faster analytics on large datasets.

4. What distinguishes a massively parallel processing system from others?
MPP is a processing paradigm in which hundreds or thousands of processing nodes work in parallel on different elements of a computational operation. Each of these nodes runs its operating system instance. They don't share memory and have their input and output devices.

Key Takeaways

Let us brief out the article.

Firstly, we saw the meaning of massive parallelism and its need. We saw the advantages of enormous parallelism and the major hardware components needed. Later, we saw the architecture of massive parallelism with some advantages and disadvantages. Lastly, we saw the working of it. That'sThat's all from the article. 

I hope you all like it.

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Topics covered
Advantages of Massive Parallelism
Major Hardware Components of Massive Parallelism
Architecture Of Massive Parallelism
Shared Disk Systems
Shared Nothing Systems
Frequently Asked Questions   
Key Takeaways