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    <p>I think the question have been answered as YES a not interpreted language will in 99.99% of the cases run faster than the same algorithm under a VM. This said (having worked a lot in image processing both in java and c/c++ where memory and time mattered) I think you should at first try to optimize your code, here are are my advises:</p> <ul> <li>Try to find the bottleneck of your code using a profiler. A lot of things we sometimes omit can be reveled with such tools (like type casting, unnecessary creation of objects, most critical functions which should be optimized at first) A profiler have to be your friend.</li> </ul> <p>Then (just few examples I could see for raytracing):</p> <ul> <li>Replace tan/sin/cos by lookup tables (as long as you can) or <a href="http://lab.polygonal.de/2007/07/18/fast-and-accurate-sinecosine-approximation/" rel="nofollow">approximate functions</a></li> <li>Try to process data per array and not per sample</li> <li>Try using multiple threads</li> <li>…</li> </ul> <p>Now those things are "good" but if the speed is really critical for you, I would not suggest to use a c or c++ language (even if you could) but more likely to focus on OpenCL. This is probably the best tool available and most adapted for building ray tracings engine. Just imagine you are not talking there of an improvement of 30% but more likely 10'000% (100x faster) Here is a java interface: <a href="http://jogamp.org/jocl/www/" rel="nofollow">http://jogamp.org/jocl/www/</a> Good luck :-)</p>
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    1. COIf you can generally notice the difference between a JITed code and code generated by a c++ compiler you're either using compiler intrinsics to do stuff neither compiler could, measure situations where the language spec forces the one language to be slower or got the measurements wrong. And getting the latter wrong is extremely simple (or other said: it's extremely hard to do correctly). Anyways: Going with a GPU solution for raytracing is obviously superior and using lookup tables on modern CPUs may not be such a great idea ;-)
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    2. COMmmm I do not get the last point: optimization is most of the time (not to say always) a memory vs cpu tradeoff. Lookup tables is only one among others: this is just a kind of manual cache (and you sure know how cache is important to get something running smoothly ;-) )
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    3. CODepends. The memory vs. cpu tradeoff is well known, but in this case computing the sin anew may be faster than looking it up in the table - especially if you've got lots of computation going on. The table also evicts some other useful data from the cache or may be evicted itself (not good for performance either). So that's something one has to benchmark to be sure.
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