Parallel programming is a double-edged sword

Today I read an interesting article at DDJ on understanding parallel performance. It outlines several truths about multithreaded code. One is that it is not always faster than non-threaded code.

Yes. It’s surprising. Several years back, when I was young(er) and single-core was the only choice of affordable PC (there were those servers with multiprocessors, but those aren’t affordable at all), I was naive to think that if I make my code multithreaded, I will gain some speed. In general that’s probably true, up to a point. Too many threads imply expensive context-switching, especially in a single core (heck, it is still a problem with multi-cores, as long as there are more threads than cores, as the article clearly pointed out). Over the years, I realized several considerations to ponder about when coding multithreaded code.

The first is how CPU-bound are your threads. If your process is highly CPU-bound, it is not worth to have more threads than cores. Remember, there are always other processes running as well, so the optimum number would be the same as the number of cores, or less as number of cores explodes. For CPU-bound processing, it is better to minimize context-switching.

Blocking process is another consideration. Any process that may block for I/O or to obtain a lock should be run on a thread pool. In this case, I would say that having 2-3 times threads compared to the number of cores are optimal for most cases. Note that for highly I/O-bound processes, more threads may be needed to fully squeeze every ounce of performance out of those cores.

Locks. When your threads require access to common resources, reduce the number of threads. Too many threads will cause lock contention everywhere and slows down processing as CPU cycles are used to acquire locks. Either minimize contention, which may not be possible on most cases, or segregate resources appropriately. For example, you may want just 1 thread to write to a common file, with any threads wanting to write to that file queue its task to that 1 thread. It’s not too easy in other cases. Although there are usually way out that minimize the number of lock contention. For example, when writing to an object, try to have different threads writing to different parts of the object, hopefully unrelated part.

In some cases, you can make a single thread perform much better than multiple threads (well, I said single thread, but I’m lying). The idea is to saturate the thread as much as you can with CPU-bound tasks. You may have some other threads (see I lied) that collect resources and writing out data as the single thread completed its tasks. In a web server example, you can have one master thread. When an incoming HTTP connection arrives, another thread will process the request into a task (perhaps a Request object?) and queue it at the master thread as it comes. The master thread may process one request and realized that it needs to read a file and queue that task to another file reader task, while at the same time returning the partially processed request back to the queue and start processing other request. Most bottleneck here will come from the queue implementation, but this is a baby example so I’m going to stop at that. Another bottleneck will be if the number of incoming requests arrive faster than the speed in which the master thread can process the requests, in which case, we arrive at classic example of dropping of requests (a la network routers and switches). In that case, it may be time to consider having 2 master threads.

From the above examples, you can probably tell that this method of self-managing your “threads” (in this case, instead of threads, we have requests) allow for much faster processing as it reduces context-switching.

What about LWPs (light-weight processes)? Indeed… what about LWPs? :) LWPs are fast, I love it.

P.S. Do read the DDJ’s article above. It’s very thoughtful.

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