Re: [eigen] Intel (R) MKL IE SpBLAS support in Eigen |

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*To*: eigen <eigen@xxxxxxxxxxxxxxxxxxx>*Subject*: Re: [eigen] Intel (R) MKL IE SpBLAS support in Eigen*From*: Gael Guennebaud <gael.guennebaud@xxxxxxxxx>*Date*: Thu, 5 Apr 2018 14:01:03 +0200*Dkim-signature*: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:in-reply-to:references:from:date:message-id:subject:to; bh=rSsT8hklI71ljgDWNJdiDwwcNndelMsCOQezV9deTfA=; b=ajSlsIwvGF7OckzgHboMnE5qoBPYuXqIcZZFjvjdB79/Xv3PYx8ykt4iv3Trp+p0vV /yJoO3h80FK/yuDLPWYwqd4SRNQra8TD+9pxlsrWtZsdpoBKLRWQJVY0gsxXa+MXABzO cZPqqBZfXAmXkURdV/9fXZPI1g+UtRJqioVQn9n8v8xp5+V7duBvyftJleaDS20RQNor Dwz9ywOIP/B1yfofCcqmQPkJEiv+Rjbs3c8kvzRKfPcgHN51onfENxFx0wXkJiqxY5Mc kGfZnRgwqZH3bbHBJ8990I7dViKIx72sot9hc8776b0W9xC10gucR7PEh3SiMoZQ1B7p V1Wg==

Thank you for opening this discussion on the public mailing list.

So let's discuss about the public API, which currently is not very convenient as already noticed by others. Issues are:

(i1) - Storing MKL's handle in SparseMatrix breaks ABI and does not sounds very generic.

- We need a way to control:

(i2) - which operations are going to be analyzed/optimized,

(i3) - and specify the 'expected_calls' parameter.

In order to discuss these issues, let's consider the following typical pattern: (e.g., non-linear optimization, eigenvalues, ...)

SimplicialLDLT<SparseMatrix<double> > llt(A);

while(...) {

...

x = llt.solve(b);

...

}

Here the triangular L factor is going to be used for triangular and transposed-triangular solves dozens to hundreds of time but only the user of SimplicialLDLT knowns that, not SimplicialLDLT, nor SparseMatrix. Moreover, the user does not own the SparseMatrix that we want to analyze/optimize for. Other patterns are likely easier to handle, so let's focus on it for now.

Regarding (i1), I would suggest to introduce a new type, say MklSparseMatrix<> that would enhance SparseMatrix<> through inheritance. Then for (i2) and (i3) we could imagine something like:

MklSparseMatrix::beginAnalysis(Index expected_calls) const {

// turn *this to compressed mode

// create handle

// store expected_calls

// enable recording mode

}

MklSparseMatrix::endAnalysis() const {

// disable recording mode

// [optional] call mkl_sparse_optimize

}

All states in MklSparseMatrix would be mutable.

Between a pair of beginAnalysis/endAnalysis each call to a supported operation would trigger calls to mkl_sparse_set_*_hint()/mkl_sparse_optimize.

Optionally, we could even add a "dryrun" mode for which no operation would be performed, only calls to mkl_sparse_set_*_hint() and then mkl_sparse_optimize would be called in endAnalysis(). This way mkl_sparse_optimize() would be called only once.

And that's it.. Our example would look-like:

SimplicialLDLT<MklSparseMatrix<double> > llt(A);

int it=0;

while(...) {

...

if(it==0) llt.matrixL().beginAnalysis(100);

x = llt.solve(b);

if(it==0) llt.matrixL().endAnalysis();

...

++it;

}

or using a "dry-run" mode:

SimplicialLDLT<MklSparseMatrix<double> > llt(A);

llt.matrixL().beginAnalysis(100, DryRun);

x = llt.solve(b); // permutation and division by the diagonal matrix D would still be performed, but calls to actual triangular solves would be by-passed

llt.matrixL().endAnalysis();

while(...) {

...

x = llt.solve(b);

...

}

If someone directly deal with the factor L, then we could follow the same pattern or copy the SparseMatrix factor L to a MklSparseMatrix:

SimplicialLLT<SparseMatrix<double> > llt(A);

MklSparseMatrix L(llt.matrixL());

L.beginAnalysis(100,DryRun);

y = L.triangularView<Lower>() * x;

L.endAnalysis();

while(...) {

...

y = L.triangularView<Lower>() * x;

...

}

This design in quite general and expendable to any sparse-optimizers, even built-in ones in the future.

In contrast to the current proposal, only selected operations would be passed to MKL (need to use a MklSparseMatrix + begin/end recording phase).

What do you think?

gael

On Tue, Apr 3, 2018 at 11:39 PM, Zhukova, Maria <maria.zhukova@xxxxxxxxx> wrote:

Hello Eigen community,

My name is Maria Zhukova and I’m a software development engineer at Intel ® MKL Sparse team.

My team is interested in contributing into Eigen, so I’ve investigated our possibilities and so far this is what I have:

Eigen support different operations for sparse matrices stored in CSR and CSC format which can be implemented on a basis of IE SpBLAS kernels (please, refer to https://software.intel.com/en-us/mkl-developer-reference-c- for the general idea of interfaces)inspector-executor-sparse- blas-routines

, basically we want to implement calls to our IE SpBLAS into next operations:SparseMatrix + SparseMatrix (mkl_sparse_?_add)

SparseMatrix * DenseVector (mkl_sparse_?_mv)SparseMatrix * DenseMatrix (mkl_sparse_?_mm)

SparseMatrix * SparseMatrix (mkl_sparse_spmm),

and Triangular solve (mkl_sparse_?_trsv).I’ve already started with implementation of sparse_time_dense_impl_mkl kernel which is based on mkl_sparse_?_mv (included in patch).

This is how it will look like for user:

#include <Eigen/SpBLASSupport><--NEW:IE SpBLAS include modulevoid main () {

SparseMatrix<double, RowMajor> A;

Matrix<double, Dynamic, 1> x, y;

A.makeCompressed();/* Convert matrix A into CSR/CSC format */

A.createSparseHandle();/* NEW: is used to create handle required for all IE SpBLAS routines */// support of IE SpBLAS is here

y = beta*y + alpha*A*x;/* call to mkl_sparse_?_mv with operation = SPARSE_OPERATION_NON_TRANSPOSE */

y = beta*y + alpha*A.transpose()*x;/* call to mkl_sparse_?_mv with operation = SPARSE_OPERATION_TRANSPOSE */

y = beta*y + alpha*A.adjoint()*x;/* call to mkl_sparse_?_mv with operation = SPARSE_OPERATION_CONJUGATE_ TRANSPOSE */

A.destroySparseHandle();/* NEW: is used to delete created handle */

}

I’ve attached a draft patch including all necessary changes and would like to hear your feedback.

Please, let me know if you have any questions and comments.

Best regards,

Maria

**Follow-Ups**:**Re: [eigen] Intel (R) MKL IE SpBLAS support in Eigen***From:*Edward Lam

**References**:**[eigen] Intel (R) MKL IE SpBLAS support in Eigen***From:*Zhukova, Maria

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