Parallel Computing Benefits enhances speed and efficiency across industries. Discover its types, and how it’s revolutionizing technology.
What is parallel computing?
Large computation problems are divided into smaller ones that can be handled concurrently by several processors in a technique called parallel computing, sometimes referred to as parallel programming.
A shared memory is used for communication across the processors, and an algorithm is used to aggregate their solutions. Compared with its predecessor, serial computing (sometimes called serial computation), which employs a single processor to tackle problems sequentially, parallel computing is far faster.
Software was designed to address issues sequentially when computers were initially created in the late 1940s and early 1950s, which limited processing speed. In order to solve issues more quickly, algorithms had to be developed and put into use using a central processing unit (CPU) that followed a set of instructions. Another instruction might not be solved until the first one has been executed.
By dividing computational issues into smaller, related problems, parallel computing began to enable computers to execute code more quickly and efficiently in the 1950s. After that, these issues known as parallel algorithms were split up across several processors.
Since parallel systems have advanced to the point that they are now found in many computers, routine operations like sending a text message or checking email can be completed hundreds of times faster than they could be with serial computing.
Parallel Computing Benefits
Lowering costs
Prior to the advent of parallel computing, serial computing required single processors to solve complicated problems step-by-step, taking minutes or hours to complete tasks that parallel computing could complete in a matter of seconds. The original iPhones, for instance, employed serial computing and could take a minute to access an email or app. Parallel computing, which was initially included in iPhones in 2011, greatly accelerates those activities today.
Complex problem solving
As computers advances and takes on increasingly challenging jobs, systems must do thousands or even millions of tasks simultaneously. The most sophisticated algorithms used in today’s ML models are distributed across numerous processors and mostly rely on parallel computing. Because a single processor can only execute one computation at a time, bottlenecks in serial computing would make machine learning tasks take a lot longer.
Faster analytics
Large-scale number crunching is accelerated by parallel computing and processing, allowing for the interactive queries that underlie data analysis. With over a quintillion bytes of data being created daily, businesses may find it difficult to sort through digital data in search of useful insights. Parallel processing sorts through data far more quickly than a serial could computing by deploying multi-core computers to a data structure.
Increased efficiencies
When computers are equipped with parallel computing, they can utilize resources much more effectively than when they are using serial computing. In order to run more applications and complete more tasks at once, today’s most advanced computer systems use many cores and processors.
How does parallel computing work?
The term “parallel computing” covers everything from pocket smartphones to supercomputers. Parallel computing uses hundreds of thousands of cores to handle hard problems like discovering a new cancer therapy or finding SETI. Simply said, parallel computing allows you to send emails from your phone more quickly than you could with a serial computing system.
Generally speaking, shared memory, distributed computing memory, and hybrid memory are the three distinct architectures utilized in parallel computing. All parallel computing architectures use the same message-passing interface (MPI), but each architecture functions differently. MPI describes message-passing software protocols for C++ and Fortran, among other computer languages. For the creation of new software and applications that depend on parallel computing capabilities, open-source MPI has proved essential.
Different parallel computing architectures
Shared memory
Laptops and cellphones are two examples of typical, everyday parallel computing applications that use shared memory architecture. Multiple processors are required to access the same shared memory resource in parallel computers with shared memory architectures.
Distributed memory
A prevalent feature of many enterprise applications, distributed memory is utilized in cloud computing architectures. A distributed system for parallel computing consists of a network connecting several processors, each with its own memory resources.
Hybrid memory
The hybrid memory architectures used by today’s supercomputers are a type of parallel computing system that combines processors with shared memory over distributed memory networks. Shared memory and tasks given to other units on the same network are accessible to connected CPUs in a hybrid memory environment.
Specialized architectures
The three primary designs are complemented by other, less popular parallel computer architectures that are intended to address more complex issues or highly specialized activities. General-purpose computing processors on graphics processing units (GPGCUs) and vector processors for arrays of data, or “vectors,” are examples of these. An essential component of deep learning (DL), the technology that powers the majority of AI applications, is CUDA, a proprietary GPGCU application programming interface (API) created by Nvidia.
Types of parallel computing
Parallel computing comes in four varieties, each with a distinct function:
Bit-level parallelism
By increasing the processor’s word size and reducing the amount of instructions it needs to execute to address an issue, bit-level parallelism works. By expanding the bit level parallelism from 4-bit processors to 8-, 16-, 32-, and 64-bit processors with each generation outperforming the previous one, computer architecture progressed until 1986. Perhaps the most well-known example of a bit-parallelism breakthrough is the Nintendo 64, which was the first 64-bit program to be utilized broadly.
Instruction-level parallelism
With instruction-level parallelism (ILP), the processor selects which instructions to execute in parallel computing. To enhance resource utilization and boost performance, ILP’s processors are designed to execute specific tasks concurrently.
Task parallelism
Task parallelism is a kind of parallel computing in which code is parallelized across multiple processors that are working on the same task at the same time. In pipelining, for instance, a number of operations are carried out on a single batch of data in order to reduce serial time by executing them concurrently.
Superword-level parallelism
The vectorization technique known as superword-level parallelism (SLP), which is applied to inline code, is more sophisticated than ILP. A parallel computing technique called vectorization is used to save time and resources by completing several related operations at once. Using SLP, redundant scalar instructions in a code block can be found and combined into a single superword operation.
Use cases of parallel computing
Many of the technologies that power its society, like chatbots, gaming consoles, smartphones, and blockchains, depend on parallel processing. The following are a few examples:
Smartphones
Many cellphones employ parallel processing to speed up processes. The iPhone 14, with its 6-core CPU and 5-core GPU, can perform 17 trillion serial computing processes per second, an unfathomable pace.
Blockchains
In order to validate transactions and inputs, blockchain technology which powers cryptocurrencies, voting machines, healthcare, and many other cutting-edge digital applications uses parallel computing to link several computers. Throughput is increased and a blockchain becomes more scalable and economical when transactions are processed concurrently rather than one at a time thanks to parallel computing.
Laptop computers
Chips having many processing cores, a basis of parallelism, are used in the most powerful laptops of today, including MacBooks, ChromeBooks, and ThinkPads. Multicore CPUs, such as the Intel Core i5 and HP Z8, enable users to run 3D graphics, edit video in real-time, and carry out other intricate, resource-intensive operations.
Internet of Things
Data from internet-connected sensors underpins the Internet of Things. Parallel computing is essential to analyze data and gain insights for complex systems including power plants, dams, and transportation networks. The development of IoT technology depends on parallel computing since traditional serial computing cannot sort through data quickly enough for IoT to function.
Artificial intelligence and machine learning
For AI applications like facial recognition and natural language processing (NLP), training machine learning (ML) models requires parallel computation. The time required to accurately train machine learning models on data is greatly decreased by parallel computing, which carries out processes concurrently.
The space shuttle
In order to manage its avionics and keep an eye on data in real time, the space shuttle’s computer system depends on five IBM AP-101 computers running in tandem. These formidable devices, which are also found in fighter jets, have a processing speed of about 500,000 commands per second.
Supercomputers
Because of their heavy reliance on parallel processing, supercomputers are frequently referred to as parallel computers. For instance, to improve human understanding of physics and the environment, the American Summit supercomputer performs 200 quadrillion operations per second.
