CPU Performance and Reproducibility#

NeurEco uses the Intel MKL math library for its CPU computations. By default, MKL runs a fast code path chosen for your processor type. This page describes the default, the processor-independent mode, and how to switch between them.

On this page

Default Mode#

By default, NeurEco chooses MKL’s code path from the processor:

  • Intel processors: MKL’s AVX2 code path (MKL_CBWR=AVX2). MKL computes the same way on every Intel processor with AVX2, with or without AVX-512.

  • AMD processors: MKL’s automatic mode, the fastest code path MKL offers on these processors (NeurEco sets MKL_CBWR=AVX2, which MKL runs as AUTO on them). On Windows, the Python package for PyTorch 2.7 uses the processor-independent mode instead (see the warning below).

On the same machine, with the same NeurEco version, data, settings and number of threads, results are reproducible from run to run. Results can differ between an Intel and an AMD processor, between Windows and Linux, and between runs that use a different number of threads. Exported models (FMU, C, ONNX, VBA) do not use MKL and are not affected.

In Python, import NeurEco before NumPy and PyTorch. PyTorch’s MKL reads MKL_CBWR once, when it starts: the conda PyTorch builds start it during import torch, and a conda NumPy built on MKL shares that MKL and starts it during import numpy. An MKL that started first does not use NeurEco’s mode, and convolutional results can then differ from run to run.

Processor-Independent Mode: MKL_CBWR=COMPATIBLE#

Set the environment variable MKL_CBWR to COMPATIBLE before you start NeurEco. MKL then uses the same code path on every Intel or AMD processor, so MKL’s results do not depend on the processor type.

What to expect:

  • Slower builds. On an AMD Ryzen AI 9 HX PRO 370 under Linux, the default mode built Tabular models in about 21% less time than this mode, ODE models in 9% to 23% less and PFS models in about 17% less. The difference depends on the processor and on the problem.

  • Results can still differ between Windows and Linux, and between runs that use a different number of threads.

Warning

On Windows, the MKL release that comes with some PyTorch builds (for example the conda PyTorch 2.7 builds, which ship MKL 2024) has a known defect on AMD processors outside the processor-independent mode: it can corrupt results or crash. This is why, on Windows, the Python package for PyTorch 2.7 uses COMPATIBLE on AMD processors. On an AMD processor, when MKL_CBWR is not COMPATIBLE and PyTorch’s MKL is older than 2025.0.1, convolutional builds print a warning that names the PyTorch and MKL versions. On such machines, set MKL_CBWR=COMPATIBLE or use PyTorch 2.9. The GUI and the neureco command include PyTorch 2.9.

In Python, the same MKL also leaves NeurEco’s mode when NumPy or PyTorch starts it before NeurEco while MKL_CBWR is not set, and NeurEco then warns at import. Import NeurEco before NumPy and PyTorch, set MKL_CBWR=COMPATIBLE before starting Python, or use PyTorch 2.9.

Setting MKL_CBWR#

NeurEco reads the setting once, when it starts. Set the variable, then start the GUI, the neureco command or Python from the same window. On Windows the installer does not add neureco.exe to the PATH: use its full path, as shown below. Write the value in capitals: MKL ignores a value it does not recognize, such as compatible, and runs AUTO instead.

set MKL_CBWR=COMPATIBLE
"C:\Program Files\Adagos\NeurEco-5.0\guibin\NeurEcoGui.exe"

To build from the command line instead, from the same window:

"C:\Program Files\Adagos\NeurEco-5.0\bin\neureco.exe" Regression build build.conf

Note

To use this mode in every session, create a user environment variable MKL_CBWR with the value COMPATIBLE (Windows: search for “Edit environment variables for your account”; Linux: add export MKL_CBWR=COMPATIBLE to ~/.profile). The variable then applies to every program that uses MKL, not only to NeurEco.

Going Back to the Default#

Remove the variable, then restart the GUI, the command or the Python session. If you created a user environment variable, delete it where you created it (Windows: “Edit environment variables for your account”; Linux: ~/.profile), then open a new window or sign in again.

Windows (Command Prompt):

set MKL_CBWR=

Windows (PowerShell):

Remove-Item Env:MKL_CBWR

Linux:

unset MKL_CBWR

Note

MKL_CBWR also accepts MKL’s other values, for example AUTO for the fastest code path of an Intel processor (such as AVX-512). On AMD processors MKL accepts only COMPATIBLE and AUTO, and runs any other value as AUTO.

Threads and OpenMP Runtimes#

NeurEco runs its CPU computations on several cores through OpenMP. Only one OpenMP runtime should be loaded in a process. When NeurEco finds more than one, it prints a warning at the start of each build: see “More than one OpenMP runtime”.

Next Steps#