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    <p>If you are bound and determined, you can explicitly spawn 24 threads using the NSThread API, and have each of those threads pull from a synchronized queue of work items. I would bet money that performance would get noticeably worse.</p> <p>GCD works at its most efficient when the work items submitted to it never block. That said, the workload you're describing is rather complex and rife with opportunities for your threads to block. For starters, you're spawning a bunch of other processes. Right here, this means that you're already relying on the OS to divvy up time/resources between your master task and these slave tasks. Other than setting the OS priority of each subprocess, the OS scheduler has no way to know which processes are more important than others, and by default, your subprocesses are going to have the same priority as their parent. That said, it doesn't sound like you have anything to gain by tweaking process priorities. I'm assuming you're blocking the master task thread that's waiting for the slave tasks to complete. That is effectively parking that thread -- it can do no useful work. But like I said, I don't think there's much to be gained by tweaking the OS priorities of your slave tasks, because this really sounds like it's an I/O bound workflow...</p> <p>You go on to describe three I/O-heavy operations ("serializing it as a set of XML files, a SQLite database, and a CoreData database.") So now you have all these different threads and processes vying for what is presumably a shared bulk storage device. (i.e. unless you're writing to 24 different databases, on 24 separate hard drives, one for each core, your process is ultimately going to be serialized at the disk accesses.) Even if you had 24 different hard drives, writing to a hard drive (even an SSD) is comparatively slow. Your threads are going to be taken off of the CPU they were running on (so that another thread that's waiting can run) for virtually any blocking disk write.</p> <p>If you wanted to maximize the performance you're getting out of GCD, you would probably want to rewrite all the stuff you're doing in subtasks in C/C++/Objective-C, bringing them in-process, and then conducting all the associated I/O using <code>dispatch_io</code> primitives. For API where you don't control the low-level reads and writes, you would want to carefully manage and tune your workload to optimize it for the hardware you have. For instance, if you have a bunch of stuff to write to a single, shared SQLite database, there's no point in ever having more than one thread trying to write to that database at once. You'd be better off making one thread (or a serial GCD queue) to write to SQLite and submitting tasks to that after pre-processing is done.</p> <p>I could go on for quite a while here, but the bottom line is that you've got a complex, seemingly I/O bound workflow here. At the highest-level, CPU utilization or "number of running threads" is going to be a particularly poor measure of performance for such a task. By using sub-processes (i.e. scripts), you're putting a lot of control into the hands of the OS, which knows effectively nothing about your workload a priori, and therefore can do nothing except use its general scheduler to divvy up resources. GCD's opaque thread pool management is really the least of your problems.</p> <p>On a practical level, if you want to speed things up, go buy multiple, faster (i.e. SSD) hard drives, and rework your task/workflow to utilize them separately and in parallel. I suspect that would yield the biggest bang for your buck (for some equivalence relation of <code>time == money == hardware</code>.)</p>
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    1. POHow can I speed up a Mac app processing 5000 independent tasks?
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    1. COGood and valid points.
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    2. COGreat analysis, I really appreciate it. I'll look into what we can do hardware-wise, as well as bringing the script work in-process.
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    3. COAlso, as you take steps to improve your performance **measure**, **measure**, **measure**. Otherwise you might be wasting time on something that barely matters. At 5K tasks in 10h, each task is taking ~7s. Get a small subset of your data, like maybe 24 tasks. Change something, process the test dataset to see what impact each change you make has. Over the long term, you can't improve what you can't measure. A good first step would be to investigate whether parallelism is helping you at all -- try doing one task at a time. How does it compare to the fully parallel case?
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