As the title says, I’ve been having a most weird server experience, culminating in a rather fascinating and unexpected discovery.

As posted recently, I’ve been experimenting with Jupyter Notebooks using JupyterHub on Ubuntu 18.04 Server.

I started with a server built on Oracle’s VirtualBox 5.x running on my development machine, which is an Intel quad-core I7 with 16GB of memory and a couple of smaller SSDs. I gave the virtual Ubuntu 8GB of memory and 2 cores, plus 64GB of disk space. This is where I cut my teeth on installing Jupyter, first locally, then JupyterLabs then JupyterHub (again locally) before finally installing JH globally. On the way I learned quite a lot, and took these lessons to all other platforms via some detailed documents I wrote.

The first Ubuntu was desktop, complete with lots of X-type stuff. It was fast, it was good, but I wanted a more “dedicated” server.

My second server was Ubuntu server running on my Windows Server 2008 R2 file server. It’s a backup file server, so it wasn’t doing much. I installed Oracle VirtualBox, this time V6.0 and Ubuntu 18.04 server as I don’t need the x-stuff and wanted a lean, faster server rather than a desktop. (the iso install images are quite dramatically different in size). This machine is a quad-core Xeon of recent vintage.

he server only had 8GB physical memory, so I could only give the virtual server 4GB. As a result, it was very slow.

About that time I resurrected a ‘pizza box’ 1U quad-core Xeon server that also had 8GB of memory (it was the max for that vintage machine). As this was a dedicated box, I could install Ubuntu 18.04 server as the native OS and give it all the memory. After installing JupyterHub, it seemed… VERY sluggish. Opening notebooks took a very long time (minutes) and sometimes they would not open at all. I experienced problems connecting to the kernel, and it was just very frustrating.

I’d deleted both virtual machines, so decided to try another on the development I7 box. Giving it 8GB, 2 cores and 64GB disk as before, I installed JupyterHub.

At this point, I have two almost identical servers, with the same memory. The quad-Xeon has 4 cores, the virtual I7 has only 2, but otherwise things are very close.

And here came the unexpected surprise. The I7 virtualmachine is easily 10x faster to my perception than the Xeon. It’s truly a night and day difference. Where the Xeon is sluggish to open notebooks and connect the kernel (if it even succeeds), the I7-virtual is quick and responsive. Editing notebooks is a joy, instead of a grind. Things are quick, kernels don’t die and it’s just a totally different environment.

Yet aside from hardware, everything about the installs is identical. Even the notebooks come from the same github repo, so are identical.

Today I ran some benchmark tests on both machines, and every test shows the I7-virtual machine (with 2 cores) is double the speed of the quad-core xeon with 4 cores. It’s astounding.