How fast is VB .net compared to native code for arithmetic?

Question

I need to write software that will do a lot of math. Mostly it will be matrix multiplication with integers to compute DCT. How much faster should I expect the code to run in native c as compared to VB .Net? Factor of 2, factor of 10, factor of 1000...? Has someone tried and collected statistics on this?

Answer 1

The .NET code is compiled into native code by the JIT compiler, so you get native code in both cases.

The difference is that the C code has somewhat less overhead around the calculations, so you should perhaps expect a performace difference of factor 2.

Answer 2

.Net code is JIT-compiled to native code before execution, so it should not be slower than native code in general. I'd expect a factor < 10.

Moreover, adaptive optimization techniques profile the code as it runs, gaining more information than a typical static compiler. So, the JIT can make more informed decisions for further optimizations

Answer 3

VB is 93.7% as fast as C. If you pick the right scenario.

Actually, if your 'native C' includes regular calls to malloc() and free(), any kind of Gargage Collected language like VB.Net is going to literally run circles around it. GC can be 10x faster than mallocs in your inner loops.

If you break down and use C, try to reuse structures that you declared just once instead of making new ones, to avoid this problem. This may be of benefit even in VB if your solution lends itself to it. However it will be harder to program and GC is very fast.

As far as bounds/overflow checks, if speed is important and testing has revealed they don't happen, and you're not risking life or millions from an error or abend, they are a waste of time. But if you can't get rid of them, your time is likely still more valuable in a language with which you can program more quickly.

If you expect serious size and usage, it pays to split the task with a controlling program and store the allocated 'task definitions' into a shared directory with a file per task solver, or a database. Then you can run a solver per processor (2 per HT CPU), or network computers. Be weary of queue structures - it's tough to atomicly 'Mark-Taken-And-Get-Data-If-Not-Taken'. You know how many task solvers you're going to start. I did this with an imaging utility I develop, it was much easier than expected, and it creamed the previous version. Plus if you use multiple processes with a properly dividable problem domain, you avoid the slight-to-significant programming burden of multithreading. Or convincing your coworkers that your culrly braces are in the right place. Peace.