Why Some Programming Languages Support Parallelism and Others Don’t

Introduction

As the world of high-performance computing continues to evolve, parallelism has emerged as an essential component in the process of software development. Through the use of parallel computing, programs are able to carry out numerous activities simultaneously, which results in an improvement in performance, particularly for applications that are CPU-bound. On the other hand, not all programming languages allow for parallelism to be embedded into their framework. Some programming languages, such as Java, Go, and C++, have sophisticated parallelism frameworks, but other languages, such as Python and Ruby, either offer limited support or rely on other libraries to accomplish parallel processing.

This article investigates the factors that contribute to the distinction between languages that have native support for parallelism and those that do not. An investigation into the philosophy of language design, runtime environments, concurrency models, and ecosystem elements that influence the availability of parallelism and the ease with which it can be implemented will be carried out.

Understanding Parallelism in Programming

Before diving into why some languages support parallelism and others don’t, it’s important to…