I’ve been thinking a lot lately about the way we accomplish multiprocessing. We’ve seen a significant change in the operation of Moore’s law for CPU speeds: today’s CPUs are about the same speed as those of a few years ago, but they have more cores, and more virtual processors on those cores. This is great for heavily-loaded servers, which have plenty of distinct tasks to place on those cores and VCPUs, but not so useful for users working with single-threaded applications.
Why are most applications still single-threaded? There are lots of good reasons. Threaded code is harder to write, and not just because it requires careful analysis and use of synchronization primitives: many common tasks are difficult to meaningfully parallelize without careful control over inter-thread communication, and in a portable application you don’t have that kind of control. Threaded code generally performs badly on single-CPU systems, which are still common. Some popular languages still make threading difficult, at least in a portable fashion. And threads are still relatively heavyweight entities in most operating systems: you don’t spawn ten threads to mergesort a 100-item array.
Some of these problems will go away with a little more time, but some will get worse. NUMA architectures can make sharing data between threads slow. Hyperthreading and its interaction with processor caches adds yet another level of unpredictability.
We know how to build massively parallel systems that run massively parallel algorithms. What is still unknown is how to build portable, simple software that can run efficiently across a vareity of architectures. This is a problem of practice, not theory, and there’s lots of interesting work going on in this area.
Of course, there are languages designed explicitly to support communication, such as Limbo or Erlang, Haskell, and Clojure. For the most part, these languages are structured as communicating sequential processes, which is to say that they represent multiprocessing as a set of sequential threads that pass information to one another. Problems of thread safety are subsumed by the languages, but mapping the parallelism to available resources is generally left to the programmer or administrator.
One interesting project is Apple’s Grand Central Dispatch. It defines a simple but highly expressive closure syntax (a block) and a mechanism to dynamically schedule execution of such closures (queues). Critically, the GCD library takes care of scaling the parallelism of the queue processing appropriately to the underlying hardware. On a single-threaded CPU, this amounts to cooperative multitasking, but on parallel hardware the operating system can dynamically allocate virtual CPUs to applications needing more parallelism.
This topic seems to come up often in my various pursuits, so I will return to it again.