Hi, Thanks everyone for the helpful comments. If this isn't the way to go then what do you recommend for a good tradeoff between fast and easy to write iterations? I have tried iterating with using the iteration protocol (start(), next(), done()) and it is very fast but was looking for something that was a bit easier to write. Can you point me to some more info on "generators"? Right now I'm just learning the language and trying to understand the various options and what the performance impact is. It would be helpful if the documents (or some other site) gave some idea of the performance of various options.
Carl On Saturday, April 2, 2016 at 8:48:18 AM UTC-4, Yichao Yu wrote: > > On Sat, Apr 2, 2016 at 8:33 AM, Erik Schnetter <[email protected] > <javascript:>> wrote: > > Can you put a number on the task creating and task switching overhead? > > The most obvious expensive part is the `jl_setjmp` and `jl_longjmp`. > Not really sure how long those takes. > There are also other overhead like finding another task to run. > > For this particular example, the task version is doing > > 1. Box a value > 2. Switch task > 3. Return the boxed value > 4. Since it is not type stable (I don't think type inference can > handle tasks), a dynamic dispatch and another boxing for the result > 5. Switch task again. > > Every single one above are way more expensive than the integer addition. > > @Carl, > > If you want to do this in this style, I believe the right abstraction > is generator (i.e. iterator). The support is added on master and > there's work on making it faster. > > > > > For example, if everything runs on a single core, task switching could > > (theoretically) happen within 100 ns on a modern CPU. Whether that is > > the case depends on the tradeoffs and choices made during design and > > implementation, hence the question. > > > > -erik > > > > On Sat, Apr 2, 2016 at 8:28 AM, Yichao Yu <[email protected] > <javascript:>> wrote: > >> On Fri, Apr 1, 2016 at 3:45 PM, Carl <[email protected] <javascript:>> > wrote: > >>> Hello, > >>> > >>> Julia is great. I'm new fan and trying to figure out how to write > simple > >>> coroutines that are fast. So far I have this: > >>> > >>> > >>> function task_iter(vec) > >>> @task begin > >>> i = 1 > >>> for v in vec > >>> produce(v) > >>> end > >>> end > >>> end > >>> > >>> function task_sum(vec) > >>> s = 0.0 > >>> for val in task_iter(vec) > >>> s += val > >>> end > >>> return s > >>> end > >>> > >>> function normal_sum(vec) > >>> s = 0.0 > >>> for val in vec > >>> s += val > >>> end > >>> return s > >>> end > >>> > >>> values = rand(10^6) > >>> task_sum(values) > >>> normal_sum(values) > >>> > >>> @time task_sum(values) > >>> @time normal_sum(values) > >>> > >>> > >>> > >>> 1.067081 seconds (2.00 M allocations: 30.535 MB, 1.95% gc time) > >>> 0.006656 seconds (5 allocations: 176 bytes) > >>> > >>> I was hoping to be able to get the speeds to match (as close as > possible). > >>> I've read the performance tips and I can't find anything I'm doing > wrong. I > >>> also tried out 0.5 thinking that maybe it would be faster with > supporting > >>> fast anonymous functions but it was slower (1.5 seconds). > >> > >> Tasks are expensive and are basically designed for IO. > >> ~1000x slow down for this simple stuff is expected. > >> > >>> > >>> > >>> Carl > >>> > > > > > > > > -- > > Erik Schnetter <[email protected] <javascript:>> > > http://www.perimeterinstitute.ca/personal/eschnetter/ >
