RE: Eigen 3.3 vs 3.2 Performance (was RE: [eigen] 3.3-beta2 released!)

[<Daniel.Vollmer@xxxxxx>]

Hi Christoph,

(I cc'd the mailing list again.)

The compilation units are rather big, so directly comparing the resulting code is difficult.

I've run the test-cases for gcc-8.1 and clang-3.8 with -msse4.2 -mtune=native to disable AVX.

This improves the situation for gcc, (except for the "tau" test-cases where it's only "close") and results in the same performance as Eigen-3.2. Disabling partial vec or enabling it doesn't seem to make a difference for that setting anymore.

For clang disabling AVX is a slight win for "tau" vs. default settings, but a slight loss for cgns (where the matrix-vector product and AD plays a bigger role, see area 2&3).


Best regards

Daniel Vollmer

--------------------------
Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
German Aerospace Center
Institute of Aerodynamics and Flow Technology | Lilienthalplatz 7 | 38108 Braunschweig | Germany

Daniel Vollmer | AS C²A²S²E
www.DLR.de

________________________________________
Von: Christoph Hertzberg [chtz@xxxxxxxxxxxxxxxxxxxxxxxx]
Gesendet: Mittwoch, 1. August 2018 12:34
An: Vollmer, Daniel
Betreff: Re: Eigen 3.3 vs 3.2 Performance (was RE: [eigen] 3.3-beta2 released!)

Hi,

could you also try compiling with `-DEIGEN_UNALIGNED_VECTORIZE=0` and
with AVX disabled, e.g., using `-msse4.2 -mtune=native` -- alternatively
also by commenting out the corresponding detection inside Eigen/Core
(this would actually be nice, if it was controllable by command-line
options).
And of course, any combinations of these options would be interesting,
if they make a difference.

If you have sufficiently small compilation units, it might also be worth
having a look at the difference between the generated assembler code --
but that is usually more productive if you had singled out a reduced
test-case.


Cheers,
Christoph





On 2018-08-01 11:10, Daniel.Vollmer@xxxxxx wrote:
> Hello everyone,
>
> with the recent release of 3.3.5 I've once again looked at upgrading from our currently used Eigen 3.2 to the current stable branch, but some performance regressions remain, which make this a difficult decision, as I'm unable to nail down the exact cause (probably because it's not a single one) and would prefer to not slow down the overall performance.
>
> I've attached a document with some performance measurements for different compilers, different Eigen versions, and 3 different test-cases for our code (tau, cgns, dg) that stress different areas / sizes.
> The "vs best" column compares run-time against the overall best run-time, "vs same" only relative to shortest run-time with the same compiler (so essentially between different Eigen variants with the same compiler).
> Eigen 3.2 version used was 3.2.9 + some backports of improvements to AutoDiffScalar
> Eigen 3.3 version used was 3.3.5.
> The tests were run on a Xeon E3-1276 v3 (with our code doing multi-threading, and Eigen configured to not use threading of its own). Minimum run-time of 4 runs.
>
> We use Eigen in a CFD code for 3 roughly distinct subject areas:
> 1) fixed-size vectors (and some matrices) of doubles, direct access to individual values (with compile-time known indices) or segments, simple linear algebra, few matrix-vector products.
> 2) same as 1, but using Eigen::AutoDiffScalar instead of double (building up a Jacobian)
> 3) Fixed-size matrix-vector products (inside of a Block-Jacobi iteration, not using any of Eigen's solvers)
>
> For the different cases:
> tau: Only uses 1), with vectors of sizes 5 and 8, matrices of size 5x5
> cgns: Uses 1)-3), with vectors of sizes 6 and 13, matrices of size 6x6 (for both 1 and 3).
> dg: Uses 1)-3), with vectors of sizes 5 and 8, matrices of size 5x5 (for 1) and 20x20 (for 3).
>
> The outcomes seem to be
> - clang is generally fastest
> - the performance regression is more pronounced for gcc
> - (partial) vectorization seems to "hurt" simple direct access (area 1), disabling it improves performance (clang) or at least reduces the impact of Eigen 3.3 (gcc)
>
> If we were only looking at clang, I'd be nearly willing to advocate moving to 3.3 (with default settings), because only a regression for the "tau" case remains.
>
> Unfortunately, I'm at a loss at how to pin-point these any more, and attempts at extracting a reduced test-case / example that exhibits the same behavior have not been fruitful, and some profiling of the actual code between Eigen 3.2 and 3.3 does not seem to directly yield actionable information.
>
> If anyone has any ideas for things to try, I'm all ears. :)
>
> Either way, thanks for your helpful (and nice to use) library!
>
>
> Best regards
>
> Daniel Vollmer
>
> --------------------------
> Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
> German Aerospace Center
> Institute of Aerodynamics and Flow Technology | Lilienthalplatz 7 | 38108 Braunschweig | Germany
>
> Daniel Vollmer | AS C²A²S²E
> www.DLR.de
>
> ________________________________________
> Von: Vollmer, Daniel
> Gesendet: Donnerstag, 28. Juli 2016 12:46
> An: eigen@xxxxxxxxxxxxxxxxxxx
> Betreff: RE: [eigen] 3.3-beta2 released!
>
> Hi Gael,
>
>> Fixed: https://bitbucket.org/eigen/eigen/commits/e35a38ad89fe/
>> With float I get a nearly x2 speedup for the above 5x5 matrix-vector
>> products (compared to 3.2), and x1.4 speedup with double.
>
> I tried out this version (ca9bd08) and the results are as follows:
> Note: the explicit solver pretty much only does residual evaluations,
> whereas the implicit solver does a residual evaluation, followed by a
> Jacobian computation (using AutoDiffScalar) and then a block-based
> Gauss-Jacobi iteration where the blocks are 5x5 matrices to
> approximately solve a linear system based on the Jacobian and the
> residual.
>
> Explicit solver:
> ----------------
> eigen-3.3-ca9bd08                 10.9s => 09% slower
> eigen-3.3-beta2                   11.1s => 11% slower
> eigen-3.3-beta2 UNALIGNED_VEC=0   10.0s => 00% slower
> eigen-3.2.9                       10.0s => baseline
>
> Implicit solver:
> ----------------
> eigen-3.3-ca9bd08                 34.2s => 06% faster
> eigen-3.3-beta2                   37.5s => 03% slower
> eigen-3.3-beta2 UNALIGNED_VEC=0   38.2s => 05% slower
> eigen-3.2.9                       36.5s => baseline
>
> So the change definitely helps for the implicit solver (which has lots
> of 5x5 by 5x1 double multiplies), but for the explicit solver the
> overhead of unaligned vectorization doesn't pay off. Maybe the use of
> 3D vectors (which used for geometric normals and coordinates) is
> problematic because it's such a borderline case for vectorization?
>
> What I don't quite understand is the difference between 3.2.9 (which
> doesn't vectorize the given matrix sizes) and 3.3-beta2 without
> vectorization: Something in 3.3 is slower under those conditions, but
> maybe it's not the matrix-vector multiplies, as it could also be
> AutoDiffScalar being slower.
>
>
> Best regards
>
> Daniel Vollmer
>
> --------------------------
> Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
> German Aerospace Center
> Institute of Aerodynamics and Flow Technology | Lilienthalplatz 7 | 38108 Braunschweig | Germany
>
> Daniel Vollmer | AS C²A²S²E
> www.DLR.de
>


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Attachment: 2018-07 Flucs Eigen Perf v2.pdf
Description: 2018-07 Flucs Eigen Perf v2.pdf