I have tried googling for the information using keywords such as sparse/random but I believe those key word may have slight different meaning to what I wanted to accomplish.
Say I have a dataset containing a billion (10^9) points (dense)
I want to read in only a subset, say a 10000 (10^5) but this subset is non-contiguous.
Is there a way to do some sort of hyperslab/selection so that one can provide a list of indices and with a single HDF5 call, H5Dread() in the data in the order as specified in the supplied indices ?
Do I have to make 10000 individual calls if the request is non-contiguous ?
Which HDF5 example code should I refer to for more information ?
P.S. I am currently able to read in data individually via hyperslab. I am hoping to find a more efficient way.
Cheers
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Nicholas Yue
Graphics - RenderMan, Visualization, OpenGL, HDF5
Custom Dev - C++ porting, OSX, Linux, Windows http://au.linkedin.com/in/nicholasyue
I have tried googling for the information using keywords such as sparse/random but I believe those key word may have slight different meaning to what I wanted to accomplish.
Say I have a dataset containing a billion (10^9) points (dense)
I want to read in only a subset, say a 10000 (10^5) but this subset is non-contiguous.
Is there a way to do some sort of hyperslab/selection so that one can provide a list of indices and with a single HDF5 call, H5Dread() in the data in the order as specified in the supplied indices ?
Do I have to make 10000 individual calls if the request is non-contiguous ?
Which HDF5 example code should I refer to for more information ?
P.S. I am currently able to read in data individually via hyperslab. I am hoping to find a more efficient way.
