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Comment by octopus | Hacker News Reader

Actually it makes sense, if you manually force the code to use only 4 threads it will take more time to finish that in the case when you let the compiler split the work for you.

On a given machine there are hundreds of threads running concurrently, the OS will let them run in time slices on the available hardware. Using the same number of threads as the number of available processors does not mean your code will be faster than when you use a larger number of threads. Obviously this is OS and compiler dependent.

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> Actually it makes sense, if you manually force the code to use only 4 threads it will take more time to finish that in the case when you let the compiler split the work for you.

While I think this is correct, I think your reasoning is off. And as far as I know, the compiler does not do anything clever here (unlike in some smarter, less mainstream languages) and you're just launching a huge number of threads.

If you use only 4 threads in a similar manner in a loop, the problem is that the background threads finish and the CPU is idle while the serial loop that creates the threads is occupied by creating and launching the threads and there are not enough threads to keep the cpu busy. (edit: this is more likely about waiting for I/O, see below).

If you're truly CPU bound, you will not gain from having a lot more threads than CPU cores (* ~1.5 for hyperthreads). If your threads are waiting for I/O, adding more will cause the OS to switch to an available thread but you gain more by using asynchronous I/O functions (like aio and epoll or kqueue, which you won't find an abstraction for in C++ stdlib). The way the code in the blog works is actually spending a whole lot of good cpu cycles for doing context switching from one thread to another and wasting a lot of memory for maintaining threads that wait for disk I/O.

What you can't see from the tutorial source is what the worker actually does (make_perlin_noise), I assume that function also does the writing to disk which would explain why you benefit from launching a huge number of threads. On the other hand, your memory use is completely unacceptable with this solution.

You should try this: split the number of images evenly and make each thread compute a number of images. Launch roughly as many threads as there are cpu cores and do not make the threads wait for disk I/O. You should see performance go up and memory use go down.

One of the patterns I've used before is to use thread pools rather than starting and stopping threads all the time. Each thread in the pool can be passed a list of the tasks it needs to do, or grab tasks from a central list as it finishes a previous one.

Then after a few runs you can tune the pool size so that you get the most out of the machine. Obviously there are still gains to be made by switching to async io, avoiding any serial choke points etc, and keeping the pool size small, but it seems to be a decent approach.

It does reduce thread switching overhead considerably, usually resulting in less time spent storing and retrieving thread contexts and more time spent actually processing the thread.

It's possible on newer intel hardware we've reached the stage where context switching is no longer much of a cost I guess...

This very much depends on what the threads are doing. Like some others have mentioned, threads are sometimes an abstraction of context and sometimes a CPU. THere is no pat answer -- unless you're doing only one kind of thing -- if you're all IO bound or all CPU bound, the answer is obvious, otherwise...

I worked on a platform that could have 200+ threads running through it, but in areas of the code where it mattered, only NCPU+epsilon threads were allowed to do intense CPU-bound work. Doing this, as you might expect, improves performance substantially when the system is loaded, while at the same time allowing one to use threads as an abstraction of context in the non-CPU bound areas of the code.

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