To reiterate a comment, you should be using the GPU, not the CPU if you are doing arithmetic scientific computing. Matlab with CUDA plugins would be much more awesome than Java or c# if Matlab licensing is not an issue. The nVidia documentation shows how to compile any CUDA function into a mex file. If you need free software, I like pycuda.
If however, GPUs are not an option, I personally like C for a lot of routines because the optimizations the compiler makes are not as complicated as JIT: you don't have to worry about whether a "class" becomes like a "struct" or not. In my experience, problems can usually be broken down such that higher-level things can be written in a very expressive language like Python (rich primitives, dynamic types, incredibly flexible reflection), and transformations can be written in something like C. Additionally, there's neat compiler software, like PLUTO (automatic loop parallelization and OpenMP code generation), and libraries like Hoard, tcmalloc, BLAS (CUBLAS for gpu), etc. if you choose to go the C/C++ route.
