To respond to David Schneider's comment, I thought about this, but it requires, as far as I can imagine, that I create a bunch of matrices in the HDF5 file and use them as part of the sparse index labels.
Also, loading into memory is not possible as the matrix is way too big ~10 Gb and eventually getting to ~1 Tb. I need to do everything out of core. The down side is that you wind up instantiating fully-populated blocks in > memory when the block didn't exist in the file. But, if you do this > piece-wise as opposed to a single H5Dread call for the entire matrix *and* > if you design your H5Dread calls to align with one or more full blocks, you > can build up the equivalent sparse representation in memory *without* > paying an initial price of instantiating a full, dense matrix and then > taring it down to build the final sparse representation. One of the major problems is the dimensionality. For a *small* dataset, we have 4,000,000 columns eventually scaling to ~10^9 columns, note that this is sparse data, so most columns are zeros per row. I am blocking in chunks of 1 row and several columns to simply read in every chunk one by one would still take way too long (read time, not a memory issue). I like the ideas, but I am afraid that the compute time will be way too much. Currently, I am at 7sec for a read and multiply per row and with millions to billions of rows, this is too long. Perhaps I didn't understand you correctly. Thanks for helping too. Aidan Plenert Macdonald Website <http://acsweb.ucsd.edu/~amacdona/> On Wed, Aug 12, 2015 at 11:35 AM, Aidan Macdonald < [email protected]> wrote: > Is this what you mean by a 'sparse format'? > > > Yes, exactly. > > However, I am not sure why you need to know how HDF5 has handled the >> chunks *in*the*file, unless you are attempting to write an out-of-core >> matrix multiply. > > > Yes, I am trying to write an out-of-core matrix multiply. > > I think you can easily determine which blocks are 'empty' by examining a >> block you've read into memory for all fill value or not. Any block which >> consists entirely of fill-value is, of course, an empty block. And, then >> you can use that information to help bootstrap your sparse matrix multiply. >> So, you could maybe read the matrix several blocks at a time, rather than >> all at once, examining returned blocks for all-fill-value or not and then >> building up your sparse in memory representation from that. If you read the >> matrix in one H5Dread call, however, then you'd wind up with a >> fully instantiated matrix with many fill values in memory *before* you >> could be being to reduce that storage to a sparse format. > > > I think, but can't prove, that if I did the check, I would create more CPU > cycles that I would save. Because I need to read, check, and then dump or > multiply out. I was hoping for something that could give me a list of the > allocated chunks, then I could do a "dictionary of keys" > <https://en.wikipedia.org/wiki/Sparse_matrix#Dictionary_of_keys_.28DOK.29> > block matrix > multiplication. > > According to Table 15 here > <https://www.hdfgroup.org/HDF5/doc/UG/10_Datasets.html>, if the space is > not allocated, then an error is thrown. So perhaps this error will be > faster than actually reading the data from disk. But to do that, I need the > fill_value undefined. I was hoping for a better way to see if the chunk is > actually allocated. > > The best way in my mind is to get some sort of list of all the chunks that > are allocated. Or a iterator that goes through them, then I can do the > block matrix multiplication. > > Aidan Plenert Macdonald > Website <http://acsweb.ucsd.edu/~amacdona/> > > On Wed, Aug 12, 2015 at 10:16 AM, Miller, Mark C. <[email protected]> > wrote: > >> Have a look at this reference . . . >> >> http://www.hdfgroup.org/HDF5/doc_resource/H5Fill_Values.html >> >> as well as documentation on H5Pset_fill_value and H5Pset_fill_time. >> >> I have a vague recollection that if you create a large, chunked dataset >> but then only write to certain parts of it, HDF5 is smart enough to store >> only those chunks in the file that actually have non-fill values within >> them. The above ref seems to be consistent with this (except in parallel >> I/O settings). >> >> Is this what you mean by a 'sparse format'? >> >> However, I am not sure why you need to know how HDF5 has handled the >> chunks *in*the*file, unless you are attempting to write an out-of-core >> matrix multiply. >> >> I think you can easily determine which blocks are 'empty' by examining a >> block you've read into memory for all fill value or not. Any block which >> consists entirely of fill-value is, of course, an empty block. And, then >> you can use that information to help bootstrap your sparse matrix multiply. >> So, you could maybe read the matrix several blocks at a time, rather than >> all at once, examining returned blocks for all-fill-value or not and then >> building up your sparse in memory representation from that. If you read the >> matrix in one H5Dread call, however, then you'd wind up with a fully >> instatiated matrix with many fill values in memory *before* you could be >> being to reduce that storage to a sparse format. >> >> I wonder if it might be possible to write your own custom 'filter' that >> you applied during H5Dread that would do all this for you as chunks are >> read from the file? It might be. >> >> Mark >> >> >> >> From: Hdf-forum <[email protected]> on behalf of >> Aidan Macdonald <[email protected]> >> Reply-To: HDF Users Discussion List <[email protected]> >> Date: Wednesday, August 12, 2015 9:05 AM >> To: "[email protected]" <[email protected]> >> Subject: [Hdf-forum] Fast Sparse Matrix Products by Finding Allocated >> Chunks >> >> Hi, >> >> I am using Python h5py to use HDF5, but I am planning on pushing into >> C/C++. >> >> I am using HDF5 to store sparse matrices which I need to do matrix >> products on. I am using chunked storage which 'appears' to be storing the >> data in a block sparse format. PLEASE CONFIRM that this is true. I couldn't >> find documentation stating this to be true, but by looking at file sizes >> during data loading, my block sparse assumption seemed to be true. >> >> I would like to matrix multiply and use the sparsity of the data to make >> it go faster. I can handle the algorithmic aspect, but I can't figure out >> how to see which chunks are allocated so I can iterate over these. >> >> If there is a better way to go at this (existing code!), please let me >> know. I am new to HDF5, and thoroughly impressed. >> >> Thank you, >> >> Aidan Plenert Macdonald >> Website <http://acsweb.ucsd.edu/~amacdona/> >> >> >> _______________________________________________ >> Hdf-forum is for HDF software users discussion. >> [email protected] >> http://lists.hdfgroup.org/mailman/listinfo/hdf-forum_lists.hdfgroup.org >> Twitter: https://twitter.com/hdf5 >> > >
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