You can make the const since they don't seem to change. For any code that's meant to be reusable, having functions take parameters tends to be a better design anyway.
On Sat, Nov 1, 2014 at 12:15 PM, Kapil <kapil6...@gmail.com> wrote: > I need those variables as globals as otherwise I will have to pass them > from one function to another all the time. > > I tried passing the arguments as function arguments and which did lead to > an increase in performance. > > Is that the only way that I don't declare them as globals and pass them as > arguments each time ? That is very cumbersome. > > For multiple processes, I am in fact using DArrays, and I am unaware how > IPC takes place in Julia, but there is there an efficient way in Julia for > the same ? > > > > Regards, > Kapil Agarwal > > On Sat, Nov 1, 2014 at 11:42 AM, Stefan Karpinski <ste...@karpinski.org> > wrote: > >> There do look to be a lot of non-const globals in that code. Not sure if >> they are used in performance critical sections of the benchmarks, but there >> are some. >> >> On Sat, Nov 1, 2014 at 9:38 AM, Tim Holy <tim.h...@gmail.com> wrote: >> >>> Your code is long enough that I, for one, don't have time to dig into it >>> myself. But as a guideline, Julia should not be massively slower than C, >>> particularly on what seem (upon casual inspection) like very >>> straightforward >>> benchmarks. >>> >>> Have you read the "Performance tips" section of the manual and used the >>> tools >>> there to investigate it yourself? >>> >>> http://docs.julialang.org/en/latest/manual/performance-tips/ >>> >>> --Tim >>> >>> On Friday, October 31, 2014 11:16:44 AM Kapil Agarwal wrote: >>> > Hi >>> > >>> > This is my first experiment with Julia and I wanted to share some >>> results. >>> > I have ported the STREAM benchmark (http://www.cs.virginia.edu/stream/) >>> to >>> > Julia. The code is available on github >>> > (https://github.com/kapiliitr/JuliaBenchmarks/blob/master/streamp.jl). >>> > >>> > I am getting the following performance results in Julia - >>> > >>> > Array size = 5000000 (elements), Offset = 0 (elements) >>> > Memory per array = 38.14697265625 MiB (= 0.03725290298461914 GiB) >>> > Total memory required = 114.44091796875 MiB (= 0.11175870895385742 GiB) >>> > Function Best Rate MB/s Avg time Min time Max time >>> > Copy: 43.0 1.885108 1.861376 1.908840 >>> > Scale: 37.1 2.166505 2.155083 2.177926 >>> > Add: 48.2 2.532873 2.487158 2.578587 >>> > Triad: 43.1 2.787225 2.784426 2.790023 >>> > >>> > I am getting the following performance results in C - >>> > >>> > Array size = 5000000 (elements), Offset = 0 (elements) >>> > Memory per array = 38.1 MiB (= 0.0 GiB). >>> > Total memory required = 114.4 MiB (= 0.1 GiB). >>> > Each kernel will be executed 3 times. >>> > Function Best Rate MB/s Avg time Min time Max time >>> > Copy: 8553.3 0.009360 0.009353 0.009366 >>> > Scale: 8248.4 0.009712 0.009699 0.009726 >>> > Add: 9490.6 0.012987 0.012644 0.013329 >>> > Triad: 9032.0 0.013540 0.013286 0.013793 >>> > >>> > >>> > Following are the results with 4 processors in Julia- >>> > >>> > Function Best Rate MB/s Avg time Min time Max time >>> > Copy: 11122.2 0.007308 0.007193 0.007423 >>> > Scale: 465.5 0.217924 0.171840 0.264008 >>> > Add: 12481.8 0.009678 0.009614 0.009742 >>> > Triad: 471.3 0.267199 0.254624 0.279775 >>> > >>> > >>> > Following are the results with 4 omp threads in C- >>> > >>> > Function Best Rate MB/s Avg time Min time Max time >>> > Copy: 11077.0 0.007228 0.007222 0.007233 >>> > Scale: 10552.7 0.007587 0.007581 0.007594 >>> > Add: 11986.9 0.010023 0.010011 0.010036 >>> > Triad: 12173.0 0.009865 0.009858 0.009872 >>> > >>> > As it can be seen that with one thread/process, performance of Julia is >>> > much less than C for all the functions. However, for multi-process >>> runs, >>> > Julia performs similar to C for Copy and Add functions but it's >>> performance >>> > hits for Scale and Triad functions. >>> > >>> > What could be the reason behind this ? Could this be a problem in my >>> > implementation or is this just the way Julia is implemented ? >>> > >>> > Thanks >>> > >>> > -- >>> > Kapil >>> >>> >> >
