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1 July 2023

WSL2 vs Ubuntu performance (with e-cores)

by Mathieu Poliquin

As you probably know if you are reading this, WSL2 (Windows Subsystem for Linux) is quite useful to be able to prototype machine learning models while on Windows, no need to dual boot and switch to Linux everytime.

Since I am using Unreal Engine as well (the editor still works best on Windows), I was considering only installing Windows 11 on my server and actually train models (not just prototyping) using WSL2 as opposed to dual booting and switching back and forth to Ubuntu 22.04.

Phoronix made a very good article about WSL2 vs native comparaison with a lot of test cases but I wanted to test for my specific use case which is Machine Learning trained on videogames

Here are the specs I used for the test

Hardware specs:

WSL2 (Ubuntu 22.04):

NVIDIA driver 536.40

wsl --version

WSL version:
Kernel version:
WSLg version: 1.0.51
MSRDC version: 1.2.3770
Direct3D version: 1.608.2-61064218
DXCore version:
Windows version: 10.0.22621.1848

Ubuntu 22.04:

NVIDIA Driver Version: 530.41.03 CUDA Version: 12.1

uname -r



Tested with GTX 1080:

Test WSL2 Ubuntu 22.04
Pong with e-cores + gpu 1468 fps/s 2080 fps/s
Pong with e-cores no gpu 658 fps/s 700 fps/s
Pong without e-cores + gpu 1466 fps/s 2071 fps/s

Tested with RTX 2060 12g:

python [MODELNAME] -d cuda -t eval
Test WSL2 (ms) Ubuntu 22.04 (ms)
llama 14.285 16.315
resnet50 33.131 32.152
resnet152 78.016 77.712
vgg16 8.937 9.153
hf_gpt2 30.318 29.933
hf_gpt2_large 144.308 147.398
hf_bert 10.810 11.136
yolov3 49.417 48.884

Ubuntu 22.04 (WSL2):


Ubuntu 22.04 (Native):


I used stable-retro library along with stable-baselines 3, with the example script you can reproduce these results. The env I used is Pong-Atari2600 but you will get similar results with other games.

First I tested with e-cores on, you can see that native Ubuntu is 33% faster. Without GPU the performance is very similar so I checked GPU usage of the previous test with nvtop and it showed that the GPU’s usage on WSL2 is lower, indicating there might be problem at the Hyper-V/NVIDIA driver level.

I will need to do some more tests to isolate the problem. While NVIDIA says they [support WSL2 for Pascal GPUs] ( the architecture is getting old and maybe driver support is less of a priority for Pascal GPUs.

EDIT: I later replaced the GTX 1080 with a RTX 2060 12G and the diference in the results between WSL and were similar

I also tested with pytorch’s benchmark repo and the results are very similar between WSL2 and Ubuntu 22.04

I think the reason why pytroch benchmark has similar results and training of models on games don’t is because benchmark uses very little host to GPU transfers and it might be possible that WSL2 doesn’t handle this kind of situation effciently compared to native Ubuntu.


I was also curious to see if performance was better without e-cores. Normally Windows 11 and linux kernel 5.18 supports Intel Thread Director and will avoid scheduling heavy tasks on e cores and optimize p-core and e-core usage when possible but some users still reported problems.

It seems the peformance with and without e-cores is the same and also native Ubuntu vs WSL2 performs relatively the same


While performance on WSL2 is not bad and improved throughout the years, I will stick to dual booting for now. Iterating as fast as possible is key so that 33% performance gain is important. Hopefully Unreal engine editor gets better support on Linux!

That said WSL2 is still solid and as mentionned earlier I still use WSL2 for prototyping and debugging on my laptop

I plan to do more varied tests and maybe do some profiling, I will update this blog post when I do

tags: WSL2 - Ubuntu - 12700k - e cores - machine learning - gpu