The history, present, and future of concurrency

In the following sub-topics, we will discuss the past, present, and future of concurrency.

The field of concurrent programming has enjoyed significant popularity since the early days of computer science. In this section, we will discuss how concurrent programming started and evolved throughout its history, its current usage in the industry, and some predictions regarding how concurrency will be used in the future.

The history of concurrency

The concept of concurrency has been around for quite some time. The idea developed from early work on railroads and telegraphy in the nineteenth and early twentieth centuries, and some terms have even survived to this day (such as semaphore, which indicates a variable that controls access to a shared resource in concurrent programs). Concurrency was first applied to address the question of how to handle multiple trains on the same railroad system, in order to avoid collisions and maximize efficiency, and how to handle multiple transmissions over a given set of wires in early telegraphy.

A significant portion of the theoretical groundwork for concurrent programming was actually laid in the 1960s. The early algorithmic language ALGOL 68, which was first developed in 1959, includes features that support concurrent programming. The academic study of concurrency officially started with a seminal paper in 1965 from Edsger Dijkstra, who was a pioneer in computer science, best known for the path-finding algorithm that was named after him.

That seminal paper is considered the first paper in the field of concurrent programming, in which Dijkstra identified and solved the mutual exclusion problem. Mutual exclusion, which is a property of concurrency control that prevents race conditions (which we will discuss later on), went on to become one of the most discussed topics in concurrency.

Yet, there was no considerable interest after that. From around 1970 to early 2000, processors were said to double in executing speed every 18 months. During this period, programmers did not need to concern themselves with concurrent programming, as all they had to do to have their programs run faster was wait. However, in the early 2000s, a paradigm shift in the processor business took place; instead of making increasingly big and fast processors for computers, manufacturers started focusing on smaller, slower processors, which were put together in groups. This was when computers started to have multicore processors.

Nowadays, an average computer has more than one core. So, if a programmer writes all of their programs to be non-concurrent in any way, they will find that their programs utilize only one core or one thread to process data, while the rest of the CPU sits idle, doing nothing (as we saw in the Example 1 – Checking whether a non-negative number is prime section). This is one reason for the recent push in concurrent programming.

Another reason for the increasing popularity of concurrency is the growing field of graphical, multimedia, and web-based application development, in which the application of concurrency is widely used to solve complex and meaningful problems. For example, concurrency is a major player in web development: each new request made by a user typically comes in as its own process (this is called multiprocessing; see Chapter 6, Working with Processes in Python) or asynchronously coordinated with other requests (this is called asynchronous programming; see Chapter 9, Introduction to Asynchronous Programming); if any of those requests need to access a shared resource (a database, for example) where data can be changed, concurrency should be taken into consideration.

The present

Considering the present day, where an explosive growth the internet and data sharing happens every second, concurrency is more important than ever. The current use of concurrent programming emphasizes correctness, performance, and robustness.

Some concurrent systems, such as operating systems or database management systems, are generally designed to operate indefinitely, including automatic recovery from failure, and not terminate unexpectedly. As mentioned previously, concurrent systems use shared resources, and thus they require some form of semaphore in their implementation, to control and coordinate access to those resources.

Concurrent programming is quite ubiquitous in the field of software development. Following are a few examples where concurrency is present:

• Concurrency plays an important role in most common programming languages: C++, C#, Erlang, Go, Java, Julia, JavaScript, Perl, Python, Ruby, Scala, and so on.
• Again, since almost every computer today has more than one core in its CPU, desktop applications need to be able to take advantage of that computing power, in order to provide truly well-designed software.

Multicore processors used in MacBook Pro computers

• The iPhone 4S, which was released in 2011, has a dual-core CPU, so mobile development also has to stay connected to concurrent applications.
• As for video games, two of the biggest players on the current market are the Xbox 360, which is a multi-CPU system, and Sony's PS3, which is essentially a multicore system.
• Even the current iteration of the $35 Raspberry Pi is built around a quad-core system.
• It is estimated that on average, Google processes over 40,000 search queries every second, which equates to over 3.5 billion searches per day, and 1.2 trillion searches per year, worldwide. Apart from having massive machines with incredible processing power, concurrency is the best way to handle that amount of data requests.

A large percentage of today's data and applications are stored in the cloud. Since computing instances on the cloud are relatively small in size, almost every web application is therefore forced to be concurrent, processing different small jobs simultaneously. As it gains more customers and has to process more requests, a well-designed web application can simply utilize more servers while keeping the same logic; this corresponds to the property of robustness that we mentioned earlier.

Even in the increasingly popular fields of artificial intelligence and data science, major advances have been made, in part due to the availability of high-end graphics cards (GPUs), which are used as parallel computing engines. In every notable competition on the biggest data science website (https://www.kaggle.com/), almost all prize-winning solutions feature some form of GPU usage during the training process. With the sheer amount of data that big data models have to comb through, concurrency provides an effective solution. Some AI algorithms are even designed to break their input data down into smaller portions and process them independently, which is a perfect opportunity to apply concurrency in order to achieve better model-training time.

The future

In this day and age, computer/internet users expect instant output, no matter what applications they are using, and developers often find themselves struggling with the problem of providing better speed for their applications. In terms of usage, concurrency will continue to be one of the main players in the field of programming, providing unique and innovative solutions to those problems. As mentioned earlier, whether it be video game design, mobile apps, desktop software, or web development, concurrency is, and will be, omnipresent in the near future.

Given the need for concurrency support in applications, some might argue that concurrent programming will also become more standard in academia. Even though specific topics in concurrency and parallelism are being covered in computer science courses, in-depth, complex subjects on concurrent programming (both theoretical and applied subjects) will be implemented in undergraduate and graduate courses, to better prepare students for the industry, where concurrency is being used every day. Computer science courses on building concurrent systems, studying data flows, and analyzing concurrent and parallel structures will only be the beginning.

Others might have a more skeptical view of the future of concurrent programming. Some say that concurrency is really about dependency analysis: a sub-field of compiler theory that analyzes execution-order constraints between statements/instructions, and determines whether it is safe for a program to reorder or parallelize its statements. Furthermore, since only a very small number of programmers truly understand concurrency and all of its intricacies, there will be a push for compilers, along with support from the operating system, to take on the responsibility of actually implementing concurrency into the programs they compile on their own.

Specifically, in the future programmers will not have to concern themselves with the concepts and problems of concurrent programming, nor should they. An algorithm implemented on the compiler-level should look at the program being compiled, analyze the statements and instructions, produce a dependency graph to determine the optimal order of execution for those statements and instructions, and apply concurrency/parallelism where it is appropriate and efficient. In short, the combination of the low number of programmers understanding and being able to effectively work with concurrent systems and the possibility of automating the design of concurrency will lead to a decrease in interest in concurrent programming.

In the end, only time will tell what the future holds for concurrent programming. We programmers can only look at how concurrency is currently being used in the real world, and determine whether it is worth learning or not: which, as we have seen in this case, it is. Furthermore, even though there are strong connections between designing concurrent programs and dependency analysis, I personally see concurrent programming as a more intricate and involved process, which might be very difficult to achieve through automation.

Concurrent programming is indeed extremely complicated and very hard to get right, but that also means the knowledge gained through the process will be beneficial and useful to any programmer, and I see that as a good enough reason to learn about concurrency. The ability to analyze the problems of program speedup, restructure your programs into different independent tasks, and coordinate those tasks to use the same resources, are the main skills that programmers build while working with concurrency, and knowledge of these topics will help them with other programming problems, as well.