How improved hardware changed programming
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As we have outlined in the previous post, the memory size and computing power available to the average programmer has increased thousands of times from the first years of his art, at least in the boundaries of a single machine (network is a common bottleneck.)
This performance improvement has made radical changes to the style we use in writing software and in its development process.
The first notable change is the progressive introduction of higher-level programming languages. C is near the raw metal of a machine, but upon it portable languages have been written, such as Java and Python. They are still third-generation programming languages but they sacrifice performance for portability by providing a virtual machine and an interpreter respectively.
This is a general trait of higher-level languages: trading machine time to save the developers' time, which with the hardware improvements is now the most expensive resource. At the time of his release to the public in 1995, Java applications were considered slow programs with an extensive memory footprint (with reasons). However today this is no longer significant for a vast set of applications, and the same is true for other high-level languages like Python and Php. Premature optimization is now the evil, not the Java Virtual Machine.
Some people say that software bloats faster than Moore's law can help it: we went to the Moon in 1969 with 4 kilobyte of Ram, now we need 100-200MB to run an operating system. But the features and power of our machines is now much greater (discounted by the amount that runs software's bloated parts), we can do things that were only dreams in 60s.
Continuos integration of software project and immediate feedback via tests are two things that derive from a large amount of computing power available. Donald Knuth is a magician in algorithms, but back in the 60s he has to write a program by hand during the day, and let the machine compile it at night. Now we have the whole process of building and testing for a moderate size program run in minutes from every code check-in. Algorithms were proved on paper: now they are tested on large datasets.
Object-oriented programming is a practice fundamentally less performant than "classical" structured programming, because it stores pointers to virtual methods in every variable, even a wrapped integer. But it lets you have a real domain model in an application, where different entities, both represented as integers, cannnot be mixed up. Few of us will start a serious enterprise application without this paradigm available: the hardware improvements again made possible to simplify the programmer's life, even if sometimes bloat would be introduced.
And the list goes on: every best practices post you can find today (also mine) is in part generated by the continuous hardware improvements that have occurred, and it is a good thing, because it means we are leveraging the machines' power. Meaningful naming for entities? Try it with an 8 character limit. Iterative development, refactoring? Made possible by the insulation layers between components, which are a form of "bloat". Distributed version control? Thank you, cheap space on hard disks.
Moore's law won't save anyone introducing bloat in software. But it makes new programming practices feasible, directly borrowing them from your dreams.
Published at DZone with permission of Giorgio Sironi, DZone MVB. See the original article here.
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