AI-Powered Parallel Computing Architecture and Its Applications

1. Introduction

In order to solve a problem more effectively and rapidly, parallel computing entails running numerous activities or processes concurrently. It tries to make use of the capabilities of contemporary computer hardware, such as distributed computing systems and multi-core processors, to process massive amounts of data and carry out complicated computations more quickly than using conventional serial computing techniques (Garland, M et al 2008). Tasks are partitioned down into smaller subtasks in parallel computing, which are then carried out simultaneously on several processing units. Individual CPU cores found in a single computer or a network of computers may serve as these processing units. Parallel computing may drastically shorten the amount of time needed to finish a job or solve a problem by dividing the burden across many processing units. Parallel computing fundamental ideas include:1. Task Decomposition: This entails segmenting a challenging issue into manageable parts that may be completed separately. Each subtask may be handled simultaneously with other subtasks since it is meant to be parallelizable. 2. Data Dependencies: In parallel computing, regulating dependencies between jobs is crucial. The sequence in which activities are completed may be impacted by the fact that certain tasks may be dependent on the outcomes of others. For accurate and reliable findings, proper synchronisation methods are needed. 3. Concurrency Control: Because many activities are active at once, effective coordination and synchronisation techniques are required to avoid conflicts and guarantee data integrity. To control concurrent access to shared resources, strategies like as locks, semaphores, and barriers are used. 4. Parallel Architectures: Both shared-memory and distributed-memory systems are examples of parallel architectures. Distributed memory systems feature several separate computers linked by a network, as opposed to shared memory systems, which have multiple processors (or cores) that share a single memory space. 5. Parallel Programming Models: A number of frameworks and models for programming make it easier to create parallel programmes. These paradigms include message-passing and multi-threading, which allow for the concurrent operation of several threads inside a single process. 6. Performance Scaling: The ability of parallel computing to enhance performance as the number of processing units rises is one of its major advantages. But in order to achieve maximum performance, variables like load balancing, communication overhead, and scalability must be carefully taken into account. 7. Amdahl's Law: According to Amdahl's Law, the percentage of a process that cannot be parallelized determines how much faster a task can be performed when it is done in parallel. It emphasises how crucial it is to pinpoint and streamline a program's time-consuming components (Amdahl, G. M 2013).