Jameson, My proposal for inargs is here: https://groups.google.com/forum/#!searchin/julia-users/inargs/julia-users/FK_29Dj4eDo/A_drp4-i8DcJ
It is not compile-time; it is run-time. And it is not a warning; it is an error to violate the constraint. And I'm not sure what you mean by "weak": there is no way to bypass it except via aliasing (i.e., passing the same actual data item as two separate arguments to a routine, once as a readonly and once not). It does have the drawback that it doesn't work on recursive data structures; it will throw an error if you declare an array of arrays as a readonly argument. In a subsequent posting in that thread, I listed three possible benefits for readonly args. Two of them (preventing blunders such as matmul arguments in the wrong order; enabling simpler reasoning about code) would be realized with enforcement of readonly either by the caller or the function. The third one (enabling automatic program transformations), however, is possible only if the function rather than the caller imposes the readonly requirement. -- Steve Vavasis On Tuesday, August 5, 2014 5:38:17 PM UTC-4, [email protected] wrote: > > Dear Julia users, > > It seems to me that Julia's distinction between a 'type' and an > 'immutable' conflates two independent properties; the consequence of this > conflation is a needless loss of performance. In more detail, the > differences between a 'type' struct and 'immutable' struct in Julia are: > > 1. Assignment of 'type' struct copies a pointer; assignment of an > 'immutable' struct copies the data. > > 2. An array of type structs is an array of pointers, while an array of > immutables is an array of data. > > 3. Type structs are refcounted, whereas immutables are not. (This is not > documented; it is my conjecture.) > > 4. Fields in type structs can be modified, but fields in immutables cannot. > > Clearly #1-#3 are related concepts. As far as I can see, #4 is completely > independent from #1-#3, and there is no obvious reason why it is forbidden > to modify fields in immutables. There is no analogous restriction in C/C++. > > This conflation causes a performance hit. Consider: > > type floatbool > a::Float64 > b:Bool > end > > If t is of type Array{floatbool,1} and I want to update the flag b in > t[10] to 'true', I say 't[10].b=true' (call this 'fast'update). But if > instead of 'type floatbool' I had said 'immutable floatbool', then to set > flag b in t[10] I need the more complex code t[10] = > floatbool(t[10].a,true) (call this 'slow' update). > > To document the performance hit, I wrote five functions below. The first > three use 'type' and either no update, fast update, or slow update; the > last two use 'immutable' and either no update or slow update. You can see > a HUGE hit on performance between slow and fast update for `type'; for > immutable there would presumably also be a difference, although apparently > smaller. (Obviously, I can't test fast update for immutable; this is the > point of my message!) > > So why does Julia impose this apparently needless restriction on immutable? > > -- Steve Vavasis > > > julia> @time testimmut.type_upd_none() > @time testimmut.type_upd_none() > elapsed time: 0.141462422 seconds (48445152 bytes allocated) > > julia> @time testimmut.type_upd_fast() > @time testimmut.type_upd_fast() > elapsed time: 0.618769232 seconds (48247072 bytes allocated) > > julia> @time testimmut.type_upd_slow() > @time testimmut.type_upd_slow() > elapsed time: 4.511306586 seconds (4048268640 bytes allocated) > > julia> @time testimmut.immut_upd_none() > @time testimmut.immut_upd_none() > elapsed time: 0.04480173 seconds (32229468 bytes allocated) > > julia> @time testimmut.immut_upd_slow() > @time testimmut.immut_upd_slow() > elapsed time: 0.351634871 seconds (32000096 bytes allocated) > > module testimmut > > type xytype > x::Int > y::Float64 > z::Float64 > summed::Bool > end > > immutable xyimmut > x::Int > y::Float64 > z::Float64 > summed::Bool > end > > myfun(x) = x * (x + 1) * (x + 2) > > function type_upd_none() > n = 1000000 > a = Array(xytype, n) > for i = 1 : n > a[i] = xytype(div(i,2), 0.0, 0.0, false) > end > numtri = 100 > for tri = 1 : numtri > sum = 0 > for i = 1 : n > @inbounds x = a[i].x > sum += myfun(x) > end > end > end > > > function type_upd_fast() > n = 1000000 > a = Array(xytype, n) > for i = 1 : n > a[i] = xytype(div(i,2), 0.0, 0.0, false) > end > numtri = 100 > for tri = 1 : numtri > sum = 0 > for i = 1 : n > @inbounds x = a[i].x > sum += myfun(x) > @inbounds a[i].summed = true > end > end > end > > function type_upd_slow() > n = 1000000 > a = Array(xytype, n) > for i = 1 : n > a[i] = xytype(div(i,2), 0.0, 0.0, false) > end > numtri = 100 > for tri = 1 : numtri > sum = 0 > for i = 1 : n > @inbounds x = a[i].x > sum += myfun(x) > @inbounds a[i] = xytype(a[i].x, a[i].y, a[i].z, true) > end > end > end > > > function immut_upd_none() > n = 1000000 > a = Array(xyimmut, n) > for i = 1 : n > a[i] = xyimmut(div(i,2), 0.0, 0.0, false) > end > numtri = 100 > for tri = 1 : numtri > sum = 0 > for i = 1 : n > @inbounds x = a[i].x > sum += myfun(x) > end > end > end > > function immut_upd_slow() > n = 1000000 > a = Array(xyimmut, n) > for i = 1 : n > a[i] = xyimmut(div(i,2), 0.0, 0.0, false) > end > numtri = 100 > for tri = 1 : numtri > sum = 0 > for i = 1 : n > @inbounds x = a[i].x > sum += myfun(x) > @inbounds a[i] = xyimmut(a[i].x, a[i].y, a[i].z, true) > end > end > end > > end > > > >
