For my science fair project I have to write a computationally-intensive algorithm that is well suited to parallelization. I have read about OpenCL and CUDA and it seems they are mainly used from C/C++. While it would not be that difficult for me to pick up a bit of C to write a simple main, I was wondering how big the performance hit would be if I used Java or Python bindings for my GPU computation? Specifically, I was more interested in the performance hit using CUDA because that's the framework I'm planning on using.
GPGPU performance in high-level languages
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In general, every time you add an abstraction layer you're loosing performance but, in the case of CUDA this is not completely true because, whether using Python or Java you'll end up writing your CUDA kernels on C/Fortran, so the performance in the GPU side will be the same as using C/Fortran (check some pyCUDA examples here)
The bad news it that Java and Python will never achieve the performance of compiled languages such as C on certain tasks, see this SO answer for a more detailed discussion about this topic. Here is a good discussion about C versus Java, also on SO.
There are many questions and discussions about performance comparison between interpreted and compiled languages, so I encourage you to read some of them.