Parallel computing

IBM Blue Gene P supercomputer

AmdahlsLaw

Optimizing-different-parts

Gustafson

Taiwania 3 Supercomputer

Nopipeline

Parallel computing is a type of computing architecture in which several processors execute or process an application or computation simultaneously. Parallel computing helps in performing large calculations by dividing the workload between more than one processor, thereby reducing the execution time of a program. It leverages the fact that many computational problems can be broken down into smaller parts, which can then be solved at the same time.

Overview

Parallel computing is based on the principle that large problems can often be divided into smaller ones, which are then solved concurrently ("in parallel"). There are several different forms of parallel computing: bit-level parallelism, instruction-level parallelism, data parallelism, and task parallelism. Bit-level parallelism concerns processors with more than one bit of data, instruction-level parallelism allows multiple instructions from the same program to be executed simultaneously, data parallelism involves performing the same operation on multiple data points simultaneously, and task parallelism refers to the simultaneous execution of different tasks on the same data set.

History

The history of parallel computing goes back far, with early instances appearing in the 1950s with the IBM and UNIVAC product lines. However, it was not until the 1970s and 1980s that parallel computing started to become more widely accessible and used, thanks to the development of more affordable and powerful microprocessors.

Types of Parallel Computing Architectures

Parallel computing architectures can be classified into several types, including:

• Shared Memory Architecture: In this model, multiple processors share a single, global memory space. They communicate by reading and writing to these shared memory locations. This model is easier to program than distributed memory architectures but does not scale as well.
• Distributed Memory Architecture: Each processor has its own private memory (distributed memory), but processors can send messages to each other to share data. This model scales well but is more difficult to program.
• Hybrid Distributed-Shared Memory: This model combines aspects of both shared and distributed memory architectures, aiming to leverage the advantages of both.

Programming Models

To exploit the capabilities of parallel computing, specific programming models have been developed. These include:

• Message Passing Interface (MPI): Used in distributed memory architectures, where processes communicate by sending and receiving messages.
• OpenMP: A set of compiler directives and an API for programming shared memory architectures.
• CUDA: A parallel computing platform and programming model invented by NVIDIA for general computing on graphical processing units (GPUs).

Applications

Parallel computing is used in a wide range of applications, from scientific research to engineering and commercial applications. It is particularly useful in fields that require large-scale computations such as climate modeling, computational physics, bioinformatics, and cryptanalysis.

Challenges

Despite its advantages, parallel computing comes with its own set of challenges, including the complexity of programming, debugging parallel programs, and the issue of data synchronization and communication overhead.

Future Directions

The future of parallel computing includes the development of new architectures, programming models, and tools that make parallel computing more accessible and efficient. With the advent of quantum computing and advancements in artificial intelligence, parallel computing is expected to play a crucial role in furthering computational capabilities.