I can confirm your observation that Julia runs faster than Matlab for some problems. Answering your three questions is not easy due to the restriction "completely avoid the other benefits of Matlab". It is questionable if a comparison of two tools is useful, if only one specific feature is considered. This is like comparing a racing care with a limousine: Of course they have a different speed. As soon as I want to transport a cardboard box, this difference is not relevant.
- Is there any possibility how to speed up (optimize) loop-for and vectorized Matlab code to get similar speed as Julia?
I tried it without success. I frequently post C-mex functions in this forum, because this is my way to squeeze bottlenecks in Matlab. But some C code cannot be a fair answer to a question about Matlab and Julia.
- MATLAB JIT engine produce typically significantly slower simple for-loop code than Julia LLVM. Why?
Because I do not have the possibility to control the way Matlab's JIT is working, I cannot guess, why it is slower than Julia.
- Is possible in future MATLAB releases expect some significant additonal speed up?
Yes. An acceleration is possible.
I've answered some questions concerning the acceleration of code in this forum. The speed of the used computer is less relevant. Even for a lot of money increasing the speed by a factor of 4 is really hard (if neither the number of cores or the RAM is the limitting factor). In opposite to this, accelerating some code by a factor of 100 to 1000 was possible in many cases just by avoiding repeated calculations and using a smarter representation of the data (like columnwise operations and avoiding the dynamic creation of variables by eval()).
Of course the speed of the programming language has an effect also. You have demonstrated an advantage of Julia a tiny test problem. This is interesting and it is a good strategy to use Julia, if it solves a problem efficiently. For my work this is not an option, because I use exhaustively tested tools, e.g. one for clinical decision making with more than 300'000 lines of Matlab code (plus comments), which was tested over 2 decaded with thousands of patients. Converting this to Julia would be a waste of time.
You see, "completely avoid the other benefits of Matlab" is a really hard limitation for writing a meaningful answer to your question. The time to solve a problem consists of:
t_solution = t_design + t_programming + t_debugging + t_documenting + ...
Therefor re-using existing toolboxes has a much more effect than the actaul run time.
But your point is important: If I create a new program e.g. to calculate collisions of galaxies using some simple loops to obtain the accelerations (and not a leap-frog-toolbox, which performs the actual calculations in an assembler routine, such that Matlab is responsible for defining the initial values only and to visualize the results), I'd try this in Julia. Then it would not help to discuss, if a future version of Matlab might be faster, because waiting for years cannot compete with minutes of runtime.
For my daily work, the power of Matlab is more important than its speed. Therefore I'm not the right one to answer your question, but I wanted to make a start at least.