Significant performance improvement of symmetric eigensolvers and SVD in Intel MKL 11.2


Intel MKL 11.2 contains a number of optimizations for Symmetric Eigensolvers and SVD. These mostly related to large matrices N>4000, 6000, and on but speedups are significant comparing to the previous MKL 11.1.  SVD brings up to 6 times (or even higher on large thread counts and matrix sizes), similarly for eigensolvers, several times could be observed.

List of related optimizations present in MKL 11.2 are:

  • Improved performance of ?(SY/HE)(EV/EVR/EVD) when eigenvectors are not needed
  • Improved performance of ?(SY/HE)(EV/EVD) when eigenvectors are needed
  • Improved performance of ?(SY/HE)RDB               
  • Added Automatic Offload for ?SYRDB on Intel® Many Integrated Core Architecture (Intel® MIC Architecture), which speeds up DSY(EV/EVD) when eigenvectors are not needed
  • Improved performance of (S/D)GE(SVD/SDD) when M>=N and singular vectors are not needed

Below performance charts showcases the performance improvements for DGESVD and DSYEV routines on Intel® Xeon® E5-2600 processors.

DGESVD MKL 11.2 performance improvement

DSYED MKL 11.2 performance improvement

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