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What distinguishes a parallel programming language from a parallel library?

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Parallel programming is a way of writing software that can run multiple tasks simultaneously on multiple processors or cores. It can improve the performance, efficiency, and scalability of applications that deal with large or complex data sets, such as scientific computing, machine learning, or big data analytics. However, parallel programming is also challenging, as it requires careful coordination, synchronization, and communication among the parallel tasks, as well as handling errors, load balancing, and resource management.

To make parallel programming easier and more accessible, there are two main approaches: parallel programming languages and parallel libraries. In this article, you will learn what distinguishes these two approaches, what are their advantages and disadvantages, and what are some examples of each.

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Kirk Lincoln

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1 Parallel programming languages

A parallel programming language is a type of programming language that has features or constructs that facilitate parallelism. For instance, it may include keywords, operators, or syntax that allow the programmer to divide the work among processors, create and manage parallel tasks, synchronize and communicate between them, and handle exceptions. Additionally, a parallel programming language may have a compiler or an interpreter that can optimize the code for parallel execution by automatically detecting parallelizable sections, generating parallel code, and distributing the workload. Examples of parallel programming languages include Fortran 90/95 (which supports array-based parallelism using intrinsic functions or directives), Erlang (a functional language that supports concurrency and distribution with lightweight processes that communicate by message passing), Haskell (a functional language with implicit parallelism utilizing annotations or strategies), and OpenMP (an extension of C, C++, and Fortran that supports shared-memory parallelism).

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Kirk Lincoln

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A parallel programming language would be most likely defined as "a language that executes simultaneously from a single threaded program". Truthfully, there isn't a mainstream language considered a "parallel programming" language, because parallelism is NOT concurrency but concurrency can technically be parallelism. Concurrency is a resource-based message-passing system, whereas parallelism is a system free of data races occurring since each operation has it's own space to breathe. A truly parallel programming language would be free of data races since everything would be executed simultaneously. VHDL is an example of a language that needs to execute all at once if needed unlike something like Go that would spin up and eventually execute

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2 Parallel libraries

A parallel library is a collection of routines or modules that provide parallel functionality for a programming language that does not have native support for parallelism. It allows the programmer to create and manage parallel tasks, synchronize and communicate among them, and distribute the data and computation. In addition, it provides tools or utilities to help with debugging, testing, and monitoring the parallel program. Examples of parallel libraries include MPI, Pthreads, CUDA, and Spark. MPI is a standard for message-passing parallelism, where processes communicate by sending and receiving messages and can run on different nodes or machines. Pthreads is a standard for thread-based parallelism, where threads share the same address space and can access shared variables or resources. CUDA is a platform for GPU-based parallelism, where kernels run on the GPU and can access different types of memory or resources. Lastly, Spark is a framework for distributed parallelism, where resilient distributed datasets (RDDs) can be transformed and processed in parallel using functional operators or actions.

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Kirk Lincoln

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Parallel libraries are one of two flavors these days, async/await or concurrency libs. These libs can have a lot of bugs and usually require the user to troubleshoot their own code. When it comes to systems like Spark or CUDA, these are time-tested, community-approved solutions highly vetted by the upper echelons of academia and technology. However, these libs can have bugs introduced into them by the user, which can be targeted by AI-coding tools and corrected eventually. The trade-off is that inefficient approaches can be corrected more easily. When using a more niche possibly functional language for parallelism, there is a higher likelyhood of writing inefficient code due to the lack of references and support.

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3 Comparison and contrast

When choosing between parallel programming languages and parallel libraries, there are several factors to consider, such as ease of use, portability, performance, and flexibility. Parallel programming languages may be easier to use and offer better performance, but may be less portable and impose restrictions on the code. Parallel libraries may require more effort but can work with different languages and offer more flexibility. However, these factors are not definitive, and they may vary depending on the language or library, the application domain, or the programmer's preference. Additionally, both languages and libraries can be combined to achieve the best of both worlds.

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Kirk Lincoln

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Some parallel programming languages offer the ability to create an executable for certain platforms, so portability isn't too much of a concern these days. Spark is best supported by another language or framework to house the system, which supports security, modularity, and access for newer systems/features. Again parallel programming languages are scarce where Haskell seems like the only language that offers a core support/solution. Obviously correct this if you know more, but I usually see async solutions embedded, or Go which is inherently concurrent & fault-tolerant. Erlang is the same as well, so truthfully use a library to achieve parallelism because you miss out on so much web support with something Haskell.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Kirk Lincoln

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Concurrency is going to fulfill the needs of anyone 9/10 times who believe something needs to be "parallel". If you work with Real-Time DoD systems, low-latency trading, or something that need multiple dependencies fulfilled simultaneously then consider something like Haskell or what would have built-in parallel support. Some might say "well C++ has parallel support", pthreads is a library, which does not qualify C++ as a "paralell" programming language. If someone were to make a truly parallel programming language that wasn't niche (CUDA, ML stuff, Spark, etc.) they could easily rule the world with it. It's such a strong problem in CS that we have whole industries dedicated to supporting parallelism in their daily driver language haha.

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What distinguishes a parallel programming language from a parallel library?

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1 Parallel programming languages

2 Parallel libraries

3 Comparison and contrast

4 Here’s what else to consider

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What distinguishes a parallel programming language from a parallel library?

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1 Parallel programming languages

2 Parallel libraries

3 Comparison and contrast

4 Here’s what else to consider

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