performance regression with Eigen 3.3.0 vs. 3.2.10?

We're just in the process of porting our codebase over to Eigen 3.3 (quite an undertaking with all the 32-byte alignment issues). However, there's a few places where performance seems to have been badly affected, contrary to expectations (I was looking forward to some speedup given the extra support for FMA and AVX...). These include eigenvalue decomposition, and matrix*matrix.transpose()*vector products. I've written two minimal working examples to demonstrate.

All tests run on an up to date Arch Linux system, using an Intel Core i7-4930K CPU (3.40GHz), and compiled with g++ version 6.2.1.

1. Eigen value decomposition:

A straightforward self-adjoint eigenvalue decomposition takes twice as long with Eigen 3.3.0 as it does with 3.2.10.

File test_eigen_EVD.cpp:

#define EIGEN_DONT_PARALLELIZE
#include <Eigen/Dense>
#include <Eigen/Eigenvalues>

#define SIZE 200
using namespace Eigen;

int main (int argc, char* argv[])
{
  MatrixXf mat = MatrixXf::Random(SIZE,SIZE);
  SelfAdjointEigenSolver<MatrixXf> eig;

  for (int n = 0; n < 1000; ++n)
    eig.compute (mat);

  return 0;
}

Test results:

eigen-3.2.10:

g++ -march=native -O2 -DNDEBUG -isystem eigen-3.2.10 test_eigen_EVD.cpp -o test_eigen_EVD && time ./test_eigen_EVD

real    0m5.136s
user    0m5.133s
sys     0m0.000s

eigen-3.3.0:

g++ -march=native -O2 -DNDEBUG -isystem eigen-3.3.0 test_eigen_EVD.cpp -o test_eigen_EVD && time ./test_eigen_EVD

real    0m11.008s
user    0m11.007s
sys     0m0.000s

Not sure what might be causing this, but if anyone can see a way of maintaining performance with Eigen 3.3, I'd like to know about it!

2. matrixmatrix.transpose()vector product:

This particular example takes a whopping 200× longer with Eigen 3.3.0...

File test_eigen_products.cpp:

#define EIGEN_DONT_PARALLELIZE
#include <Eigen/Dense>

#define SIZE 200
using namespace Eigen;

int main (int argc, char* argv[])
{
  MatrixXf mat = MatrixXf::Random(SIZE,SIZE);
  VectorXf vec = VectorXf::Random(SIZE);

  for (int n = 0; n < 50; ++n)
    vec = mat * mat.transpose() * VectorXf::Random(SIZE);

  return vec[0] == 0.0;
}

Test results:

eigen-3.2.10:

g++ -march=native -O2 -DNDEBUG -isystem eigen-3.2.10 test_eigen_products.cpp -o test_eigen_products && time ./test_eigen_products

real    0m0.040s
user    0m0.037s
sys     0m0.000s

eigen-3.3.0:

g++ -march=native -O2 -DNDEBUG -isystem eigen-3.3.0 test_eigen_products.cpp -o test_eigen_products && time ./test_eigen_products

real    0m8.112s
user    0m7.700s
sys     0m0.410s

Adding brackets to the line in the loop like this:

    vec = mat * ( mat.transpose() * VectorXf::Random(SIZE) );

makes a huge difference, with both Eigen versions then performing equally well (actually 3.3.0 is slightly better), and faster than the unbracketed 3.2.10 case. So there is a fix. Still, it's odd that 3.3.0 would struggle so much with this.

I don't know whether this is a bug, but I guess it's worth reporting in case this is something that needs to be fixed. Or maybe I was just doing it wrong...

Any thoughts appreciated. Cheers, Donald.

EDIT

As pointed out by ggael, the EVD in Eigen 3.3 is faster if compiled using clang++, or with -O3 with g++. So that's problem 1 fixed.

Problem 2 isn't really a problem since I can just put brackets to force the most efficient order of operations. But just for completeness: there does seems to be a flaw somewhere in the evaluation of these operations. Eigen is an incredible piece of software, I think this probably deserves to be fixed. Here's a modified version of the MWE, just to show that it's unlikely to be related to the first temporary product being taken out of the loop (at least as far as I can tell):

#define EIGEN_DONT_PARALLELIZE
#include <Eigen/Dense>
#include <iostream>

#define SIZE 200
using namespace Eigen;

int main (int argc, char* argv[])
{
  VectorXf vec (SIZE), vecsum (SIZE);
  MatrixXf mat (SIZE,SIZE);

  for (int n = 0; n < 50; ++n) {
    mat = MatrixXf::Random(SIZE,SIZE);
    vec = VectorXf::Random(SIZE);
    vecsum += mat * mat.transpose() * VectorXf::Random(SIZE);
  }

  std::cout << vecsum.norm() << std::endl;
  return 0;
}

In this example, the operands are all initialised within the loop, and the results accumulated in vecsum, so there's no way the compiler can precompute anything, or optimise away unnecessary computations. This shows the exact same behaviour (this time testing with clang++ -O3 (version 3.9.0):

$ clang++ -march=native -O3 -DNDEBUG -isystem eigen-3.2.10 test_eigen_products.cpp -o test_eigen_products && time ./test_eigen_products
5467.82

real    0m0.060s
user    0m0.057s
sys     0m0.000s

$ clang++ -march=native -O3 -DNDEBUG -isystem eigen-3.3.0 test_eigen_products.cpp -o test_eigen_products && time ./test_eigen_products
5467.82

real    0m4.225s
user    0m3.873s
sys     0m0.350s

So same result, but vastly different execution times. Thankfully, this is is easily resolved by placing brackets in the right places, but there does seem to be a regression somewhere in Eigen 3.3's evaluation of operations. With brackets around the mat.transpose() * VectorXf::Random(SIZE) part, the execution times are reduced for both Eigen versions to around 0.020s (so Eigen 3.2.10 clearly also benefits in this case). At least this means we can keep getting awesome performance out of Eigen!

In the meantime, I'll accept ggael's answer, it's all I needed to know to move forward.

For the EVD, I cannot reproduce with clang. With gcc, you need -O3 to avoid an inlining issue. Then, with both compiler, Eigen 3.3 will deliver a 33% speedup.

EDIT my previous answer regarding the matrix*matrix*vector product was wrong. This is a shortcoming in Eigen 3.3.0, and will be fixed in Eigen 3.3.1. For the record I leave here my previous analysis which is still partly valid:

As you noticed you should really add the parenthesis to perform two matrix*vector products instead of a big matrix*matrix product. Then the speed difference is easily explained by the fact that in 3.2, the nested matrix*matrix product is immediately evaluated (at nesting time), whereas in 3.3 it is evaluated at evaluation time, that is in operator=. This means that in 3.2, the loop is equivalent to:
for (int n = 0; n < 50; ++n) {
  MatrixXf tmp = mat * mat.transpose();
  vec = tmp * VectorXf::Random(SIZE);
}
and thus the compiler can move tmp out of the loop. Production code should not rely on the compiler for this kind of task and rather explicitly moves constant expression outside loops.

This is true, except that in practice the compiler was not smart enough to move the temporary out of the loop.

1. Eigen value decomposition:

2. matrixmatrix.transpose()vector product:

EDIT

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1. Eigen value decomposition:

2. matrix*matrix.transpose()*vector product:

EDIT

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2. matrixmatrix.transpose()vector product: