https://github.com/ahwillia/Einsum.jl or https://github.com/Jutho/TensorOperations.jl might also be of interest
Op vrijdag 20 mei 2016 17:17:08 UTC+2 schreef Matt Bauman: > > I believe something just like that is already implemented in > Devectorize.jl's @devec macro. Try: `@devec y[:] = y + a .* x[ind]`. > > https://github.com/lindahua/Devectorize.jl > > On Friday, May 20, 2016 at 11:04:34 AM UTC-4, [email protected] wrote: >> >> I'm not sure that fixed-size array solves the problem. For one thing, >> the size of the small array may not be known at compile time. For another, >> I don't want the small vectors necessarily to be immutable. >> >> I decided that what would really solve this problem is indicial notation. >> This can be provided by a macro. I have not actually written the macro-- >> macro-writing sometimes gives me a headache. But I have already documented >> it! See: >> >> https://github.com/StephenVavasis/Indicial.jl >> <https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FStephenVavasis%2FIndicial.jl&sa=D&sntz=1&usg=AFQjCNHXjC8cNNi7_jdVi1QPzKUvFFeMTQ> >> >> for documentation of my latest vaporware. If someone out there who >> enjoys macro-writing wants to take a crack at this before I do, feel free! >> >> -- Steve Vavasis >> >> >> >> >> On Thursday, May 19, 2016 at 9:47:12 PM UTC-4, [email protected] wrote: >>> >>> The two functions test4 and test5 below are equivalent, but test5 is >>> much faster than test4. Apparently test4 is carrying out a heap allocation >>> on each iteration of the j-loop. Why? In general, which kinds of >>> assignment statements of the form <array>=<array> create temporaries, and >>> which don't? (In the example below, if the indirect addressing via array i >>> is eliminated, then the two functions have comparable performance.) >>> >>> Thanks, >>> Steve Vavasis >>> >>> function test4(n) >>> y = [2.0, 6.0, 3.0] >>> i = [1, 2, 3] >>> z = [0.0, 0.0, 0.0] >>> u = 0.0 >>> for j = 1 : n >>> z[:] = y[i] >>> u += sum(z) >>> end >>> u >>> end >>> >>> function test5(n) >>> y = [2.0, 6.0, 3.0] >>> i = [1, 2, 3] >>> z = [0.0, 0.0, 0.0] >>> u = 0.0 >>> for j = 1 : n >>> for k = 1 : 3 >>> z[k] = y[i[k]] >>> end >>> u += sum(z) >>> end >>> u >>> end >>> >>> >>> julia> @time Testmv.test4(10000000) >>> 1.071396 seconds (20.00 M allocations: 1.192 GB, 7.03% gc time) >>> 1.1e8 >>> >>> julia> @time Testmv.test5(10000000) >>> 0.184411 seconds (4.61 k allocations: 198.072 KB) >>> 1.1e8 >>> >>>
