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Subject: Re: [ublas] Status of development /Benchmarks
From: Athanasios Iliopoulos (athanasios.iliopoulos.ctr.gr_at_[hidden])
Date: 2013-12-09 09:08:20

what do you have in mind in 2.2?

On 12/07/2013 04:10 PM, oswin krause wrote:
> Hi,
> I like your ideas. But i am not sure what you mean with 1. and 2.
> The problem I see is, that uBLAS right now doesn't offer the desired
> quality and speed for single core, let alone multi core. Therefore I
> will focus on the internal infrastructure instead of motivating new
> algorithms.
> short description:
> 1. Rock solid implementation of the BLAS algorithms( "compute kernels")
> 2. Rewriting of the expression mechanic.
> 2.1 automatic use of the compute kernels for the operations
> 2.2 automatic optimisation of expressions
> long description
> 1. Rock solid implementation of the BLAS algorithms: matrix x vector
> and matrix x matrix in all useful combinations (sparse/sparse
> dense/dense dense/sparse ... triangular/dense...etc there are a lot),
> but also: matrix assignment. uBLAS will not be compared in #Features
> but in runtime compared to Armadillo/Eigen/Atlas/GotoBlas. Having a
> fast implementation of this is also crucial for multi-core and
> distributed computations. Also all higher level decompositions
> (Cholesky, LU, SVD, RankDecomp,...) and triangular solvers rely on
> fast BLAS.
> 2. Rewriting of the expression mechanic. The way uBLAS is implemented
> right now, writing
> noalias(A)+=prod(B,C)+D
> or
> vec = prod(prod<matrix>(B,C),vec2);
> is in most cases a bad decision. The second expression wouldn't even
> compile without the template parameter.
> What I would like to have is the following:
> 2.1 automatic use of the fast algorithms. That is the above expression
> should be translated to
> axpy_prod(B,C,A,false);
> noalias(A) +=D;
> right now the matrix_assign algorithms are used which do a terrible
> job on prod(B,C).
> 2.2 automatic optimization of expressions. The second expression above
> is really inefficient. First the matrix-matrix prod is evaluated and
> the result is afterwards used for a single matrix-vector prod.
> now compare to this:
> prod(B,prod<vector>(C,d));
> only 2 matrix-vector products are performed which might save 99% of
> the FLOPS of the first expression. So it would be better if this would
> be transformed automatically. Normally the user could do that himself
> but sometimes it is not possible. Consider a Conjugate Gradient Solver:
> x=CG(A,b);
> a usual pattern of A is A=XX^T+c*Identity. CG uses internally only
> matrix-vector products - and possibly not many of them. Therefore you
> don't want to compute A because it is a) slow b) might require more
> memory than is available. Without expression optimizations it would be
> impossible to write a good CG that could do that.
> Greetings,
> Oswin
> On 07.12.2013 11:38, David Bellot wrote:
>> Hi,
>> it has been a long discussion we all had for many months now. Should
>> we rewrite ublas from scratch or simply improve it.
>> Joaquim and Oswin wants a brand new ublas
>> Nasos was more in favor of improving it.
>> I personally find very exciting the idea of writing something new,
>> but ublas is very well known now. On the other hand, Eigen and
>> Armadillo took the crown of the main C++ blas library in users' hearts.
>> On my todo list for ublas, there are things that will require ublas
>> to be deeply changed. At this stage, we can almost talk about a new
>> library.
>> Christmas is very close now, so maybe it's a good time to start
>> talking about the features we wish for ublas and see if they can be
>> implemented with the current version or if a new uBLAS 2.0 is necessary.
>> After all, Boost::signal did the same a few years ago. We can
>> definitively do the transition.
>> I begin:
>> - unified representation of vectors and matrices to represent the
>> fact that a vector IS a matrix. Matlab does the same
>> - automated use of different algorithm to let the compiler "chooses"
>> the best implementation (if possible) and switch on SSE, distributed
>> or whateve we need
>> - implementation of solvers and decompositions algorithms
>> and this is what Nasos and I think should be integrated too:
>> 1. Matrix multiplication
>> 2. Algorithms infrastructure (so that we can have real useful features)
>> 3. Matrix/vector views for interoperability <- I think this is ultra
>> critical because now ublas is monolithic in the sense that you have
>> to use it everywhere you manipulate data. This would really help into
>> letting people for example have a list of vectors (they are plotting)
>> and ublas working on top of that to do for example transformations
>> 4. NEW DOCUMENTATION - examples and the rest
>> 5. Incorporate some critical bindings (i.e. mumps bindings which is
>> currently probably the most efficient smp and distributed open source
>> linalg solver)
>> 6. matlab binding?
>> 7. distributed ublas
>> Please add and ESPECIALLY, please tell me your view on the current
>> infrastructure of uBLAS. It seems many people are not happy with the
>> current "expression template" grammar.
>> I'm open to everything
>> Best,
>> David
>> On Thu, Dec 5, 2013 at 11:14 AM, Joaquim Duran <jduran.gm_at_[hidden]
>> <mailto:jduran.gm_at_[hidden]>> wrote:
>> I think that al stuff pending of merge listed by David, should be
>> merged and migrate to uBlas 2.0 and while uBlas 2.0 is in
>> development/maintenance then design from scratch uBlas 3.0.
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> Sent to: athanasios.iliopoulos.ctr.gr_at_[hidden]

Dr Athanasios Iliopoulos
Research Assistant Professor
George Mason University
Resident at Computational Multiphysics Systems Lab. Code 6394
Center of Computational Material Science
Naval Research Laboratory
4555 Overlook Ave. SW, Washington DC 20375
Tel : +1 202 767 2165
e-mail: athanasios.iliopoulos.ctr.gr_at_[hidden]