By far the most likely issue is that you're doing everything in global scope. The next most likely problem is that you have some type stability/predictability issues. And of course, it's always possible that you have code that Matlab is really fast at; for some problems it's not possible to do much better than a well-written vectorized code that leverages fast kernels.
On Fri, Jun 12, 2015 at 11:31 AM, Spencer Russell <[email protected]> wrote: > There are a bunch of useful tips for making your Julia code faster here: > http://docs.julialang.org/en/release-0.3/manual/performance-tips/ > > There tends to be a lot of discussion on the list on relative performance > between Julia and other languages, and the core team keeps track of > performance issues very carefully, so it's definitely not just a case of > nobody noticing that Julia is actually slower than Matlab. :) > > Most likely there are things you can do in your Julia code to speed it up > (sometimes by orders of magnitude), though I'm sure there are specific > cases where optimized Matlab code could beat out optimized Julia code, or > at least where the performance is equivalent. > > It's also worth noting that often if you take Matlab code that's pretty > well-written and port it directly to Julia you'll see worse performance, > because the things you do in Julia to make your code fast are different > than the things you do in Matlab to make your code fast. Again the > performance tips linked above are super helpful. > > -s > > > On Fri, Jun 12, 2015, at 04:26 AM, [email protected] wrote: > > Without a specific example it's hard to say anything. Matlab can be faster > for some cases, but Julia should be faster in many cases, if you write your > code correctly. > > >
