Using MKL efficiently

The Intel Math Kernel Library is a popular library for vector and matrix operations, solving eigenvalue problems, and much more. On AMD processors it is important that you verify whether MKL is using its best performing routines. You can also force it to and below we discuss how this can be done.

MKL performs run time checks at startup to select the appropriate Intel processor. When it cannot work ut the processor type a least common instruction set is set is selected yielding lower performance.

AVX2 vector units

Each processor core on Betzy has two vector units, these are 256 bits wide and can hence operate on four 64-bit floating point numbers simultaneously. With two such units and selecting fused multiply add (FMA) up to 16 double precision operations can be performed per clock cycle.

This yields a marketing theoretical performance of frequency times number of cores times 16, 2.26 GHz * 128 * 16 = 4608 Gflops/s for a single compute node (or 6.2 Pflops/s for the complete Betzy). No program consists of only FMA instruction so these numbers are inflated.

In any case the vector units are important for floating point performance, see below.

Mixing Intel compiler and MKL versions

Users are advised to check if there is any performance difference between Intel 2019b and the 2020 versions. We recommend to not mix different compiler versions and Math Kernel Library (MKL) versions, e.g. building using 2020 compilers and then linking with MKL 2019.


To instruct MKL to use a more suitable instruction set a debug variable can be set, e.g. export MKL_DEBUG_CPU_TYPE=5.

However, the MKL_DEBUG_CPU_TYPE environment variable does not work for Intel compiler distribution 2020 and and newer.

Forcing MKL to use best performing routines

MKL issue a run time test to check for genuine Intel processor. If this test fail it will select a generic x86-64 set of routines yielding inferior performance. This is well documented here and remedies are discussed in Intel MKL on AMD Zen.

It has been found that MKL calls a function called mkl_serv_intel_cpu_true() to check the current CPU. If a genuine Intel processor is found, it returns 1.

The trick is to bypass this by writing a dummy function which always returns 1 and place this first in the search path (below we show how):

int mkl_serv_intel_cpu_true() {
	return 1;

Save this into a file called trick.c and compile it into a shared library using the following command: gcc -shared -fPIC -o trick.c

To put the new shared library first in the search path we can use a preload environment variable: export LD_PRELOAD=<path to lib>/

In addition, setting the environment variable MKL_ENABLE_INSTRUCTIONS to AVX2 can also have a significant effect on performance. Just changing it to AVX can have a significant negative impact.

Setting it to AVX512 and launching it on AMD it does not fail, MKL probably tests if the requested feature is available.

The following table show the recorded performance obtained with the HPL (the top500) test using a small problem size and a single Betzy node:




1.2858 Tflop/s


2.7865 Tflop/s


2.0902 Tflop/s


2.7946 Tflop/s