My SC11 diary 7

As a cursory glance at #SC11 on twitter today will tell you, it is now only two weeks until SC11 (or less if you count the parts of the show that start over the weekend).

So perhaps this is a good time to consider the many supercomputing people who won't be joining the hordes in Seattle this year.

My SC11 diary 6

Today was supposed to be a day away from the email, laptop, phone, etc. But it didn't quite turn out that way. Among other things, SC11 planning required some attention. Will try harder tomorrow (there won't be a diary entry tomorrow for example).

Which raises a question  - do you find time for a day off at SC? Some people arrive over the weekend and take a day away from supercomputing to do some local tourism. Others stay on an extra day or two after the end of SC for the same reason. Personally, unless flight schedules force an extra day or two, I don't normally do this.

My SC11 diary 5

I enjoy seeing the reactions of people attending SC for the first time. Perhaps being used to only attending other HPC events around the world, they are unprepared for the scale of the event.

Especially the exhibition. I am sure people get lost in there - properly lost, not just the few minutes disorientation that we all get several times in the SC11 week as we traverse the show floor looking for a specific booth or exit. Each year, I remember by about the 3rd day (but not the 1st!) that the booth numbers are partly logical - they are related to the rows/columns of the booth location within the hall. How handy for navigation!

My SC11 diary 4

Another good day with the SC11 schedule today - a good increase in the proportion of nailed down meetings.

Something that comes up during the process of arranging meeting at SC is the different logistics. There seems to be quite a variety of opinion as to the best way to attend SC week.

My SC11 diary 3

Well, shock, today so far has not been dominated by SC11! "Normal" work (and admin) has been the focus so far today. It is easy at this time of year to scan the headlines in the main HPC news outlets such as HPC Wire, InsideHPC, twitter (!), ... and assume SC is the only thing the HPC world is thinking of right now. The same is true of article preparation emails circulating for specialist publications like The Exascale Report. And it is even true to some extent for publications with a broader remit - e.g. Scientific Computing.

My SC11 diary 2

One goal for the end of each week leading up to SC11 is to have a net increase in certainty of my schedule for SC11 week itself. That means I hope for a reduction in the number of schedule entries labelled "hold for ..." or "tentative", etc. Of course, this usually also means an increase in the number of confirmed meetings, breakfast meetings, dinner meetings and so on. I also look for confirmation of my speaking slots, time to visit bits of the technical program, any media duties, and social events.

My SC11 diary 1

The SC11 conference, or just "supercomputing", will be held in Seattle this November. For many in the high performance computing community, SC is the big event of the year. Certainly it is the one that attracts the most press (and press releases), the most attendees, the biggest exhibition, and absorbs the most amount of time in planning before we even get there. It is the event where we get to meet with many of our customers, most of our potential suppliers, and many friends and collaborators.

Supercomputers and other large science facilities

In my recent HPCwire feature, I wrote that I occasionally say, glibly and deliberately provocatively, that if the scientific community can justify (to funders and to the public) billions of dollars, large power consumptions, lots of staff etc for domain specific major scientific intrusments like LHC, Hubble, NIF, etc, then how come we can’t make a case for a facility needing comparable resources but can do wonders for a whole range of science problems and industrial applications?

There is a partial answer to that ...

What happened to High Productivity Computing?

How to make HPC more effective? Value for money and high impact strategic research facilities like HPC are often difficult to match. Not so long ago, this concern meant that the familiar HPC acronym was hijacked to mean "High Productivity Computing", to emphasize that it is not only the raw compute performance at your disposal that counts but, more importantly, how well you are able to make use of that performance. In other words: how productive is it?

What is this HPC thing?

[Originally posted on The NAG Blog]


I’m sure something like this is familiar to many readers of this blog. The focus here is HPC, but there is a similar story for mathematicians, numerical software engineeers, etc.


You've just met an old acquaintance. Or a family member is curious. Or at social events (when social means talking to real people not twitter/facebook). We see that question coming. We panic. Then the family/friend/stranger, asks it. We freeze. How to reply? Can I get a meaningful, ideally interesting, answer out before they get bored? What if I fail to get the message across correctly? Oops, this pause before answering has gone on too long. Now they are looking at me strangely. They are thinking the answer is embarrassing or weird. This is not a good start.


The question? “What do you do then?” Followed by: “Oh! So what exactly is supercomputing then?”

Big Data and Supercomputing for Science

It is interesting to note the increasing attention “big data” seems to be getting from the supercomputing community.

Data explosion

We talk about the challenges of the exponential increase in data, or even an “explosion of data”. This is caused by our ever-growing ability to generate data. More powerful computational resources deliver finer resolutions, wider parameter studies, etc. The emergence of individual scale HPC (GPU etc.) that is both cost-viable and effort-viable gives increased data creation capability to the many scientists not using high end supercomputers. And instrumental sources continue to improve in resolution and speed.

So, we are collecting more data than we have before. We are also increasing our use of multiple data sources – fusion from various sensors and computer models to form predictions or study scientific phenomena.

It is also common to questions such as: are we drowning in volume of data? Is this growth in data overwhelming our ability to extract useful information or insight? Is the potential value of the increased data lost by our inability to manage and comprehend it? Does having more data mean more information – or less due to analysis overload? Do the diversity of formats, quality, and sources further hinder data use?