Hi, I just start to use pycuda to do some gpu computing.
However, I found that transfering numpy arrays to gpu costs a lot of time and so does compiling the source. I am using the SourceModule now and as far as I know, for example, I have a file called try.py and a kernel function called searching(float *arr), the question is 1) Everytime I run the try.py, the searching function is compiled once, and cached later until the codes end. So I am wondering if I can perminantly save that function and load the saved function so that I don't have to compile it when I run the script. 2) Is there a way that make transfering data faster? I read the documents, is the managed memory gonna help with this? Thanks a lot for help. Best Regards, Yuan Chen
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