[Boost-bugs] [Boost C++ Libraries] #4033: optimized matrix products

Subject: [Boost-bugs] [Boost C++ Libraries] #4033: optimized matrix products
From: Boost C++ Libraries (noreply_at_[hidden])
Date: 2010-03-22 21:38:28

#4033: optimized matrix products
 Reporter: guwi17 | Owner: guwi17
     Type: Patches | Status: new
Milestone: Boost 1.43.0 | Component: uBLAS
  Version: Boost 1.42.0 | Severity: Optimization
 Keywords: matrix multiply, performance |
 proposal from Jörn Ungermann:


 The (lacking) performance of sparse-matrix products was quite often
 noted on this list.
 Currently there are several functions which implement matrix products.
 Each is efficient under certain conditions only and some of them are not
 even documented.
 I think it important for users to have one (near-)optimal function only.
 The attached file contains an improved axpy_prod that matches the
 performance of "prod", "axpy_prod" and "sparse_prod" and is thereby
 optimal for all matrix types.


 The optimal choice of kernel for a matrix product depends on the
 properties of all involved matrices. The current implementations
 specialize only on the type of target matrix. By also determining the
 kernel depending on the source matrices, one can choose the most
 efficient kernel.
 My aim was to create a single function to match the performance of prod,
 axpy_prod and sparse_prod for all combinations of
 compressed_matrix/matrix and column/row-majority.
 The other matrix types should also be handled efficiently by my product,
 too, but I did check it only spuriously, as it should be obvious that
 those types are more suited for matrix setup, not for actual
 My axpy_prod implementation (called axpy_prod2 in the attached file)
 does not offer support for the undocumented triangular_expression stuff
 contained in the original axpy_prod, which however seems to be buggy in
 the current code for dense matrices.
 The kernels are largely based on existing kernels with one or two new
 ones being thrown in. They are as abstract as possible to handle
 arbitrary expressions efficiently. Specializing, e.g. directly on
 compressed_matrix would give another very significant performance boost,
 but also many more additional kernels, possibly more than could be
 maintained, especially as the transposedness might have to be handled
 explicitly, too.

 It would be very nice, if someone could rewrite prod/prec_prod to handle
 matrix products in the same way as my axpy_prod2 does, but I did not
 look deep enough into the expression-templates to do this myself or to
 even know if this were possible.

 In fact, I'd propose to have two sets of interfaces:

 1) One convenient one, possibly compromising efficiency
 2) One modeled closely after C-BLAS, delivering utmost efficiency.

 The latter one could then be *very easily* overloaded by the numeric
 bindings for dense matrices. I added a possible generic implementation
 for a gemm call that could be trivially overloaded for dense matrices
 and forwarded to, e.g., ATLAS numeric bindings.

 If one could achieve the same efficiency and automatic (i.e. by
 including a header) coupling to numeric bindings using only *one*
 interface, I'd prefer that naturally. However currently, we have not
 just two, but too many product functions (prod, prec_prod, axpy_prod,
 sparse_prod, opb_prod, block_prod).

 The following table gives the result for all 64 combinations of
 compressed_matrix/matrix and row/column-majorities for the three
 involved in this case 2000x2000 matrices.

 com_rm is a compressed_matrix of row_major type.
 den_cm is a matrix of column_major type.
 The  4th column indicates the used kernel.
 The  5th column gives the runtime for axpy_prod2  ( clock()/1000 )
 The  6th column gives the runtime for sparse_prod ( clock()/1000 )
 The  7th column gives the runtime for axpy_prod   ( clock()/1000 )
 The  8th column gives the runtime for prod        ( clock()/1000 )
 The 10th column gives the speedup of axpy_prod2 compared to sparse_prod.
 The 11th column gives the speedup of axpy_prod2 compared to axpy_prod.
 The 12th column gives the speedup of axpy_prod2 compared to prod.

 Larger matrix sizes result in prohibitive runtimes for the "slow"
 products, but can be used to analyse pure-sparse products.

 The runtime shall be taken only qualitatively.

 One can see that the only cases where the new implementation is slower
 are of relatively small runtime, so it may be negligible. sparse_prod
 uses an optimization that is very efficient if the target matrix has few
 off-diagonal elements, but is very inefficient if it does.
 The results will therefore vary depending on the test matrices.

 It is also obvious to see, why some people complain about product
 performance, as especially axpy_prod and prod are sometimes ridiculously
 slow for sparse matrices and sparse_prod does not seem to be documented
 in the special products section.
 My favorite case is "com_cm, com_cm, den_rm" one, which actually
 occurred in the diagnostics part of our application and was the reason
 why we started looking into this topic.

Ticket URL: <https://svn.boost.org/trac/boost/ticket/4033>
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