Don't use a macro, just use the @anon macro to create an object that will be fast to use as a "function."
--Tim On Wednesday, March 25, 2015 01:00:27 PM Phil Tomson wrote: > I have a couple of instances where a function is determined by some > parameters (in a JSON file in this case) and I have to call it in this > manner. I'm thinking it should be possible to speed these up via a macro, > but I'm a macro newbie. I'll probably post a different question related to > that, but would a macro be feasible in an instance like this? > > On Wednesday, March 25, 2015 at 12:35:20 PM UTC-7, Tim Holy wrote: > > There have been many prior posts about this topic. Maybe we should add a > > FAQ > > page we can direct people to. In the mean time, your best bet is to search > > (or > > use FastAnonymous or NumericFuns). > > > > --Tim > > > > On Wednesday, March 25, 2015 11:41:10 AM Phil Tomson wrote: > > > Maybe this is just obvious, but it's not making much sense to me. > > > > > > If I have a reference to a function (pardon if that's not the correct > > > Julia-ish terminology - basically just a variable that holds a Function > > > type) and call it, it runs much more slowly (persumably because it's > > > allocating a lot more memory) than it would if I make the same call with > > > the function directly. > > > > > > Maybe that's not so clear, so let me show an example using the abs > > > > function: > > > function test_time() > > > > > > sum = 1.0 > > > for i in 1:1000000 > > > > > > sum += abs(sum) > > > > > > end > > > sum > > > > > > end > > > > > > Run it a few times with @time: > > > julia> @time test_time() > > > > > > elapsed time: 0.007576883 seconds (96 bytes allocated) > > > Inf > > > > > > julia> @time test_time() > > > > > > elapsed time: 0.002058207 seconds (96 bytes allocated) > > > Inf > > > > > > julia> @time test_time() > > > elapsed time: 0.005015882 seconds (96 bytes allocated) > > > Inf > > > > > > Now let's try a modified version that takes a Function on the input: > > > function test_time(func::Function) > > > > > > sum = 1.0 > > > for i in 1:1000000 > > > > > > sum += func(sum) > > > > > > end > > > sum > > > > > > end > > > > > > So essentially the same function, but this time the function is passed > > > > in. > > > > > Running this version a few times: > > > julia> @time test_time(abs) > > > elapsed time: 0.066612994 seconds (32000080 bytes allocated, 31.05% > > > > > > gc time) > > > > > > Inf > > > > > > julia> @time test_time(abs) > > > elapsed time: 0.064705561 seconds (32000080 bytes allocated, 31.16% > > > > gc > > > > > time) > > > > > > Inf > > > > > > So roughly 10X slower, probably because of the much larger amount of > > > > memory > > > > > allocated (32000080 bytes vs. 96 bytes) > > > > > > Why does the second version allocate so much more memory? (I'm running > > > Julia 0.3.6 for this testcase) > > > > > > Phil
