Archive for January, 2017

January 19th, 2017

There are lots of Widgets in ipywidgets. Here’s how to list them

from ipywidgets import *

At the time of writing, this gave me

{'Jupyter.Accordion': ipywidgets.widgets.widget_selectioncontainer.Accordion,
 'Jupyter.BoundedFloatText': ipywidgets.widgets.widget_float.BoundedFloatText,
 'Jupyter.BoundedIntText': ipywidgets.widgets.widget_int.BoundedIntText,
 'Jupyter.Box': ipywidgets.widgets.widget_box.Box,
 'Jupyter.Button': ipywidgets.widgets.widget_button.Button,
 'Jupyter.Checkbox': ipywidgets.widgets.widget_bool.Checkbox,
 'Jupyter.ColorPicker': ipywidgets.widgets.widget_color.ColorPicker,
 'Jupyter.Controller': ipywidgets.widgets.widget_controller.Controller,
 'Jupyter.ControllerAxis': ipywidgets.widgets.widget_controller.Axis,
 'Jupyter.ControllerButton': ipywidgets.widgets.widget_controller.Button,
 'Jupyter.Dropdown': ipywidgets.widgets.widget_selection.Dropdown,
 'Jupyter.FlexBox': ipywidgets.widgets.widget_box.FlexBox,
 'Jupyter.FloatProgress': ipywidgets.widgets.widget_float.FloatProgress,
 'Jupyter.FloatRangeSlider': ipywidgets.widgets.widget_float.FloatRangeSlider,
 'Jupyter.FloatSlider': ipywidgets.widgets.widget_float.FloatSlider,
 'Jupyter.FloatText': ipywidgets.widgets.widget_float.FloatText,
 'Jupyter.HTML': ipywidgets.widgets.widget_string.HTML,
 'Jupyter.Image': ipywidgets.widgets.widget_image.Image,
 'Jupyter.IntProgress': ipywidgets.widgets.widget_int.IntProgress,
 'Jupyter.IntRangeSlider': ipywidgets.widgets.widget_int.IntRangeSlider,
 'Jupyter.IntSlider': ipywidgets.widgets.widget_int.IntSlider,
 'Jupyter.IntText': ipywidgets.widgets.widget_int.IntText,
 'Jupyter.Label': ipywidgets.widgets.widget_string.Label,
 'Jupyter.PlaceProxy': ipywidgets.widgets.widget_box.PlaceProxy,
 'Jupyter.Play': ipywidgets.widgets.widget_int.Play,
 'Jupyter.Proxy': ipywidgets.widgets.widget_box.Proxy,
 'Jupyter.RadioButtons': ipywidgets.widgets.widget_selection.RadioButtons,
 'Jupyter.Select': ipywidgets.widgets.widget_selection.Select,
 'Jupyter.SelectMultiple': ipywidgets.widgets.widget_selection.SelectMultiple,
 'Jupyter.SelectionSlider': ipywidgets.widgets.widget_selection.SelectionSlider,
 'Jupyter.Tab': ipywidgets.widgets.widget_selectioncontainer.Tab,
 'Jupyter.Text': ipywidgets.widgets.widget_string.Text,
 'Jupyter.Textarea': ipywidgets.widgets.widget_string.Textarea,
 'Jupyter.ToggleButton': ipywidgets.widgets.widget_bool.ToggleButton,
 'Jupyter.ToggleButtons': ipywidgets.widgets.widget_selection.ToggleButtons,
 'Jupyter.Valid': ipywidgets.widgets.widget_bool.Valid,
 'jupyter.DirectionalLink': ipywidgets.widgets.widget_link.DirectionalLink,
 'jupyter.Link': ipywidgets.widgets.widget_link.Link}
January 12th, 2017

If you are a researcher and are currently writing scripts or developing code then I have a suggestion for you. If you haven’t done it already, get yourself a willing volunteer and send them your code/analysis/simulation/voodoo and ask them to run it on their machine to see what happens. Bonus points are awarded for choosing someone who uses a different operating system from you!

This simple act is one of the things I recommend in my talk Is Your Research Software Correct and it can often help improve both code and workflow.

It quickly exposes patterns that are not good practice. For example, scattered references to ‘/home/walkingrandomly/mydata.dat’ suddenly don’t seem like a great idea when your code buddy is running windows. The ‘minimal tweaking’ required to move your analysis from your machine to theirs starts to feel a lot less minimal as you get to the bottom of the second page of instructions.

Crashy McCrashFace

When I start working with someone new, the first thing I ask them to do is to provide access to their code and simple script called runme or similar that will build and run their code and spit out an answer that we agree is OK. Many projects stumble at this hurdle! Perhaps my compiler is different to theirs and objects to their abuse (or otherwise) of the standards or maybe they’ve forgotten to include vital dependencies or input data.

Email ping-pong ensues as we attempt to get the latest version…zip files with names like get thrown about while everyone wonders where Bob is because he totally got it working on Windows back in 2009.

git clone

‘Hey Mike, just clone the git repo and run the test suite. It should be fine because the latest continuous integration run didn’t throw up any issues. The benchmark code and data we’d like you to optimise is in the benchmarks folder along with the timings and results from our most recent tests. Ignore the papers folder, that just reproduces all of the results from our recent papers and links to Zenodo DOIs’


‘Are you OK Mike?’

‘I’m…..fine. Just have something in my eye’


January 10th, 2017

I work at The University of Sheffield where I am one of the leaders of the new Research Software Engineering function. One of the things that my group does is help people make use of Sheffield’s High Performance Computing cluster, Iceberg.

Iceberg is a heterogenous system with around 3440 CPU cores and a sprinkling of GPUs. It’s been in use for several years and has been upgraded a few times over that period. It’s a very traditional HPC system that makes use of Linux and a variant of  Sun Grid Engine as the scheduler and had served us well.


A while ago, the sysadmin pointed me to a goldmine of a resource — Iceberg’s accounting log. This 15 Gigabyte file contains information on every job submitted since July 2009. That’s more than 7 years of the HPC usage of 3249 users — over 46 million individual jobs.

The file format is very straightforward. There’s one line per job and each line consists of a set of colon separated fields.  The first few fields look like something like this:

The username is field 4 and the number of slots used by the job is field 35. On our system, slots correspond to CPU cores. If you want to run a 16 core job, you ask for 16 slots.

With one line of awk, we can determine the maximum number of slots ever requested by each user.

gawk -F: '$35>=slots[$4] {slots[$4]=$35};END{for(n in slots){print n, slots[n]}}' accounting > ./users_max_slots.csv

As a quick check, I grepped the output file for my username and saw that the maximum number of cores I’d ever requested was 20. I ran a 32 core MPI ‘Hello World’ job, reran the line of awk and confirmed that my new maximum was 32 cores.

There are several ways I could have filtered the number of users but I was having awk lessons from David Jones so let’s create a new file containing the users who have only ever requested 1 slot.

gawk -F: '$35>=slots[$4] {slots[$4]=$35};END{for(n in slots){if(slots[n]==1){print n, slots[n]}}}' accounting > users_where_max_is_one_slot.csv

Running wc on these files allows us to determine how many users are in each group

wc users_max_slots.csv 

3250  6498 32706 users_max_slots.csv

One of those users turned out to be a blank line so 3249 usernames have been used on Iceberg over the last 7 years.

wc users_where_max_is_one_slot.csv 
2393  4786 23837 users_where_max_is_one_slot.csv

That is, 2393 of our 3249 users (just over 73%) over the last 7 years have only ever run 1 slot, and therefore 1 core, jobs.

High Performance?

So 73% of all users have only ever submitted single core jobs. This does not necessarily mean that they have not been making use of parallelism. For example, they might have been running job arrays – hundreds or thousands of single core jobs performing parameter sweeps or monte carlo simulations.

Maybe they were running parallel codes but only asked the scheduler for one core. In the early days this would have led to oversubscribed nodes, possibly up to 16 jobs, each trying to run 16 cores.These days, our sysadmin does some voodoo to ensure that jobs can only use the number of cores that have been requested, no matter how many threads their code is spawning. Either way, making this mistake is not great for performance.

Whatever is going on, this figure of 73% is surprising to me!

Thanks to David Jones for the awk lessons although if I’ve made a mistake, it’s all my fault!

Update (11th Jan 2017)

UCL’s Ian Kirker took a look at the usage of their general purpose cluster and found that 71.8% of their users have only ever run 1 core jobs.