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I have a neural network written in Erlang, and I just bought a GeForce GTX 260 card with a 240 core GPU on it. Is it trivial to use CUDA as glue to run this on the graphics card?
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To execute any CUDA program, there are three main steps: Copy the input data from host memory to device memory, also known as host-to-device transfer. Load the GPU program and execute, caching data on-chip for performance. Copy the results from device memory to host memory, also called device-to-host transfer.
Although there are many applications where GPUs can bring some game-changing benefits, you might say there are still some obstacles. However, Java and GPUs can do great things together.
In a typical CUDA program, data are first send from main memory to the GPU memory, then the CPU sends instructions to the GPU, then the GPU schedules and executes the kernel on the available parallel hardware, and finally results are copied back from the GPU memory to the CPU memory. ...
No, using CUDA is not a trivial matter.
The CUDA programming model basically uses C (with some additions) but in order to get the most of the GPGPU's capabilities you would have to ensure that your algorithms follow the CUDA guidelines. (see NVidia CUDA Programming Guide)
For example in order to get the best memory performance (somewhere around 70Gbps) you need to access memory in streaming mode with coalescing, also branches are very costly on the GPUs so you should avoid conditionals as much as possible. Check out the guide and samples provided with the SDK, they'll provide an excellent starting point
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