Re: GSOC 2013

Hi all,

I support Riccardo's idea. I like ublas to be template'd as much as 
possible. Since it runs effectively with small matrix. For medium size 
matrix (100 < n <1000), I would go to a blas/lapack binding. For bigger 
size (>1000), I will go for a separate package (petsc/trilinos). Making 
ublas effective globally, I think, personally, not a good idea, 
providing that other linear algebra package has long duration of 
development and performs stably/effectively.

BR
Bui


On 03/26/13 22:06, Riccardo Rossi wrote:
> Dear Oswin,
>            while you are for sure right on the performance side, you 
> should recognise that linking to blas is the proverbial "pain in the 
> ass". ever tried to do it in windows 64 without having a commercial 
> fortran compiler? try, enjoy and report...
>
> on my side ublas is really good as it allows u doing operations with 
> small matrices (either of fixed size or of variable size but still 
> small) in a simple and portable way.
>
> such operations should use effectively the cache and hence NOT be 
> memory bound. for this reason eigen, blitz++, etc can be faster than 
> blas...
>
> i really wish there was some work in having ublas to be competitive 
> with eigen for such small matrices. my only point is that i would NOT 
> mix the concept of vectors and of matrices.
>
>
> furthermore if i was to wish something than i would really love to 
> have the possibility to have such small matrices as elements of a CSR 
> matrix... then you would have a computation bound spmv (or spmm) which 
> would be very nice for many applications.
>
> anyhow...
> greetings to everyone
>
> Riccardo
>
>
>
>
> On Tue, Mar 26, 2013 at 8:56 PM, oswin krause 
> <[email protected] 
> <mailto:[email protected]>> wrote:
>
>     Hi,
>
>     there is one more thing i want to comment, and this is on the more
>     serious side:
>
>
>     On 23.03.2013 16:15, Nasos Iliopoulos wrote:
>>     David,
>>     Since mdsd:array is a generic multi-dimensional container it is
>>     not bound to algebraic operations. I expect that with proper
>>     aligned memory allocation and SSE aglorithms (It is easy to add a
>>     custom storage container that supports that) it will be as fast
>>     as MKL, GotoBLAS, Eigen or armadillo. I believe that within that
>>     context, a GSOC project will need to include both the matrix
>>     container and the SSE algorithms tasks, or even AVX.
>>     (http://en.wikipedia.org/wiki/Advanced_Vector_Extensions)
>>
>
>     and also the starting post from David itself:
>
>
>     On 23.03.2013 13:47, David Bellot wrote:
>>     OK, the idea behind this is to have a clean framework to enable
>>     optimization based on SSE, Neon, multi-core, ... you name it.
>
>     Just to make this clear: in the current state of the library, SSE,
>     AVX, multi core computation etc won't cut it as soon as the
>     arguments involved are bigger than ~32KB. In this case, uBLAS
>     performance is memory bound.Thus we will only wait more efficient
>     for the next block of memory. And even if it were not, the way
>     ublas is designed makes it impossible to use vectorization aside
>     from the c-style functions like axpy_prod, which can in 99% of all
>     relevant cases be mapped on  BLAS2/BLAS3 calls of the optimized C
>     libraries(which give you AVX/SSE and OpenMP for free). If you
>     expect that SSE helps you when computing your
>
>     A+=prod(B,C);
>
>     than you will be desperately disappointed in the current design.
>
>     Now maybe some of you are thinking: "But all fast linear algebra
>     libraries are using SSE, so you must be wrong". Simple answer:
>     these libraries are not memory bound as they optimize for that
>     (you can experience this yourself by comparing the performance of
>     copying a big matrix to transposing it. Than try the transposition
>     block-wise: allocate a small buffer, say 16x16 elements, and than
>     read 16x16 blocks from the matrix, write them transposed into the
>     buffer and than copy the buffer to the correct spot in the target
>     matrix. this gives a factor 7 speed-up on my machine. no SSE, no
>     AVX.).
>
>     Don't trust me, trust the writer of the gotoblas library:
>
>     Goto, Kazushige, and Robert A. Geijn. "Anatomy of high-performance
>     matrix multiplication." /ACM Transactions on Mathematical Software
>     (TOMS)/ 34.3 (2008): 12.
>
>     We all don't have enough time to implement fast linear algebra
>     algorithms. Instead we should fall back to the numeric bindings as
>     often as possible and use the power of expression templates to
>     generate an optimal sequence of BLAS2/BLAS3 calls.
>
>     I would also like to part in that if it happens.
>
>     Greetings,
>     Oswin
>
>
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>
>
>
> -- 
>
> Dr. Riccardo Rossi, Civil Engineer
>
> Member of Kratos Team
>
> International Center for Numerical Methods in Engineering - CIMNE
> Campus Norte, Edificio C1
>
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>
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>
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>
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>
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