We’re already seeing increasing use of many cores on client desktops. It is a change that has been long predicted. It is not just a change in architecture, but our notions of efficiency in a program. No longer can we focus on the asymptotic complexity of an algorithm by counting the steps that a single core processor would take to execute it. Instead we’ll soon be more concerned about the scalability of the algorithm and how well we can increase the performance as we increase the number of cores. This may even lead us to throw away our most efficient algorithms, and switch to less efficient algorithms that scale better. We might even be willing to waste cycles in order to speculatively execute at the algorithm rather than the hardware level.
State is the big headache in this parallel world. At the hardware level, main memory doesn’t necessarily contain the definitive value corresponding to a particular address. An update to a location might still be held in a CPU’s local cache and it might be some time before the value gets propagated. To get the latest value, and the notion of “latest” takes a lot of defining in this world of rapidly mutating state, the CPUs may well need to communicate to decide who has the definitive value of a particular address in order to avoid lost updates. At the user program level, this means programmers will need to lock objects before modifying them, or attempt to avoid the overhead of locking by understanding the memory models at a very deep level.
I think it’s this need to avoid statefulness that has led to the recent resurgence of interest in functional languages. In the 1980s, functional languages started getting traction when research was carried out into how programs in such languages could be auto-parallelised. Sadly, the impracticality of some of the languages, the overheads of communication during this parallel execution, and rapid improvements in compiler technology on stock hardware meant that the functional languages fell by the wayside. The one thing that these languages were good at was getting rid of implicit state, and this single idea seems like a solution to the problems we are going to face in the coming years.
Whether these languages will catch on is hard to predict. The mindset for writing a program in a functional language is really very different from the way that object-oriented problem decomposition happens – one has to focus on the verbs instead of the nouns, which takes some getting used to.
There are a number of hybrid functional/object languages that have been becoming more popular in recent times. These half-way houses make it easy to use functional ideas for some parts of the program while still allowing access to the underlying object-focused platform without a great deal of impedance mismatch. One example is F# running on the CLR which, in Visual Studio 2010, has because a first class member of the pack. Inside Visual Studio 2010, the tooling for F# has improved to the point where it is easy to set breakpoints and watch values change while debugging at the source level.
In my opinion, it is the tooling support that will enable the widespread adoption of functional languages – without this support, people will put off any transition into the functional world for as long as they possibly can. Without tool support it will make it hard to learn these languages.
One tool that doesn’t currently support F# is Reflector. The idea of decompiling IL to a functional language is daunting, but F# is potentially so important I couldn’t dismiss the idea. As I’m currently developing Reflector 6.5, I thought it wise to take four days just to see how far I could get in doing so, even if it achieved little more than to be clearer on how much was possible, and how long it might take. You can read what happened here, and of the insights it gave us on ways to improve the tool.