by Marcin Wichary on Flickr

Many people have heard some version of “Moore’s Law” – that the number of transistors on a chip approximately doubles every two years. This study reveals a related trend in energy consumption per computation.

There are some details to consider, of course. For starters, it helps to know that everything a computer does is broken down into individual arithmetic operations. When talking about how buff a supercomputer is, for example, we talk about the number of floating point operations it can do per second (today they run upwards of one million billion operations – what we call Peta-flops). This study doesn’t take modern supercomputers into account, but the researchers note that today’s supercomputers are made up of clusters of individual processors, some of which they did measure.

Now, your computer isn’t always working to it’s full potential. When it does, it typically doesn’t respond well to user interaction (like when you start all of your apps in the morning). So to perform the measurements, the researchers had to keep the processors busy for the period during which they measured energy consumption. They also disabled the displays on the laptops that they measured to put them on equal footing with the other computers.

In addition to these modern measurements, the researchers incorporated historical power consumption data from some of the original vacuum tube supercomputers and other earlier computer models that aren’t available today. The methods used in these prior studies varied, but the graph resulting from this aggregate study shows a remarkably clear trend:

J. Koomey et al., "Assessing Trends in the Electrical Efficiency of Computation over Time", Submitted to IEEE Annals of the History of Computing: August 5, 2009, http://www.greenercomputing.com, Dec 12, 2009

The vertical axis here is logarithmic and labeled in scientific notation. For those unfamiliar, the number after the + sign corresponds to the power of ten, so 1.E+01 means 10, 1.E+03 means 1000, and so on. Each horizontal line represents a ten-fold increase over the line below.

Note that the early vacuum tube computers operated at less than a Mega-flop per kilowatt hour. These machines could perform ten thousand calculations using the same amount of energy as a 100 Watt lightbulb left on for an hour. You can see a jump in efficiency as transistors and integrated circuits replaced vacuum tubes around 1960. And as we have packed more and more computational power into the same amount of space on a microchip, the distance that electrons have had to travel without doing useful computation has steadily been reduced, roughly doubling the number of calculations that could be done with the same amount of energy every 18 months (very similar to the trend in speed that we’re familiar with). So, although your laptop can’t do a Peta-flop in a second, it can do it using only about 1KWh of electricity – a billion times more efficiently than those vacuum tube supercomputers (although we should note that manufacturing cost is not factored in). This is the power of exponential growth.

And according to a recent interview with Lorie Wigle of Intel, while this trend continues into the future, manufacturers will be looking at additional ways to improve the efficiency of information technology, from power supply design to power management via the operating system. Hopefully this will be combined with higher rates of e-Waste recycling, leading to more sustainable computing practices.

Photo by gullevek via Flickr

Coincidentally, I also recently came across the USA National Phenology Network. Phenology (not to be confused with phrenology), is the study of phenophases (recurring plant and animal life cycle events). Whereas Weathernews was running the “My Sakura” project to collect data from citizen scientists, the NPN collects data from the public on a variety of species through the “My NPN” project. They do have a preferred set of “calibration species”, but their database includes dozens of species that can be tracked using the system. Because phenophases are sensitive to changes in climate, recording and studying phenological observations can reveal the ways in which our ecosystems change in response to global warming.

In addition to creating a massive dataset, this project is great for another reason. Maybe it’s just the scientist in me, but I get excited about going out and making the observations. I have two of the calibration species in my yard, and I’m starting to think of them as amazing organic instruments. In addition to the foliage and blossoms, there is a beauty to the unfolding growth process that I’m appreciating more by learning about the phenophases and making observations over the long term.

The convergence of nature, technology, and research in these projects is really quite remarkable. I hope that we see many similar efforts in the future.

That’s why I was pretty intrigued by a new tool called Moody that lets you “Mood Tag” your iTunes playlists with how these songs make you feel.

What a great idea! With this tool you simply click a colored square to assign a degree of “mood” to each song as you are listening to them. This morning I was air drumming along to “Lucretia MacEvil” on my way into work… Personally I’d tag that with Intense and Happy! However, chillin’ out with my Sunday morning paper and cup of coffee seems to be more in line with some Stan Getz bossa nova grooves (i.e. Calm, Happy).

Here at the museum we’ve been working on ways to use social tagging to help people find our art more easily and to be able to describe it in their own words. You can see this in action by checking out any of our artworks online. One with some great tags is “Young Woman in Blue” by Edgar Degas.

You can also checkout our TagCloud and click on any term to find other art from our collection that users have tagged. I think this can be a great way to stumble onto new works of art that you might otherwise have a hard time finding with a search tool.

I think it would be really interesting to start collecting thoughts from our visitors about what their emotion or moods are related to particular pieces of art as well! Moody gets it right by using the squares as a metaphore for mood instead of making people come up with their own words to describe this. This sort of non-text based tagging will be really useful as we try to come up with new ways of using the social tagging data that well collect from our visitors.

There are several of us in the museum community thinking about ways to use social tagging and what it means for museums to solicit information from and listen to their users in these ways. IMA and a group of other instiutions has been funded to do research into this as part of a project called steve.museum (http://www.steve.museum) Please check out the website for more information!

Or better yet! Help us with our current round of tagging experiments! You can pop on and tag some art by registering at http://tagger.steve.museum You’ll be shown some art from a number of world class institutions and asked to tag them with your own descriptions. Your tags will help us better understand how we can use these methods to make art easier to find and more engaging in an online setting. Thanks!