Bench Press

Previously at Bench Press we’ve written about the power of distributive computing and it’s ability to pool resources from volunteers over the internet to tackle projects on protein folding and the search for extraterrestrial intelligence. As distributed computing approaches mainstream, numerous projects focusing on a variety of questions have emerged.

One project that caught my eye is the Quake-Catcher Network (QCN). The network is described as

a collaborative initiative for developing the world’s largest, low-cost strong-motion seismic network by utilizing sensors in and attached to internet-connected computers.

The QCN utilizes accelerometers attached to computers to monitor for vibrations. Vibrations detected by the accelerometer are then recorded and compared to readings from other computers in the network. Only when a sufficient number of computers report comparable readings at the same time will the data be reported as an earthquake. Most recently the QCN detected a magnitude 4.4 earthquake in the Los Angeles area yesterday morning. The data generated from QCN participants can be seen here.

The beauty of the QCN is the enormous cost savings their approach can provide in comparison to traditional seismic networks like those run by the USGS. New accelerometers are now much more affordable. Sensors that plug into a USB port can cost less than $50. In addition, an increasingly common feature for laptops is a built in accelerometer to detect sudden movements like drops in order to shut down components to protect them from damage. These accelerometers can be utilized and provide a fairly large potential participant base who merely need to install BOINC and join the QCN project to begin providing data to the network.

As the number of QCN participants grows the heads of the project, Drs. Elizabeth Cochran and Jesse Lawrence, hope the network will provide not only a wealth of data for geologists but potentially a small bit of warning in the event of a large earthquake for those miles away from the epicenter. Currently, Drs. Cochran and Lawrence are working hard to increase the number of participants while also providing educational tools for use in schools to teach about earthquakes and science behind them.

Living in San Diego I think I’m in a prime location to help out so I look forward to contributing some data to the QCN (magnitude 4.0 or less please!).

(Join the Quake-Catcher Network)(Source – LA Times)

As the problems scientists solve become more and more complex, so do their demands for computational power. One approach to addressing this has been to build faster, more powerful computers, potentially with chips better suited to performing advanced calculations (like graphics cards or IBM’s Cell processor). But, this approach has serious limitations — mainly that it’s expensive to build and to maintain these supercomputers.

Some researchers, however, have turned to a radically different approach. Instead of building a bigger, better mousetrap to deal with more mice, the distributed computing approach takes the approach of placing many small, cheap mousetraps. The result is cheap “supercomputers” which are able to “pool” the computing power of many computers connected over a network.

This approach has been used by projects like Folding@Home and SETI@Home which are able to combine computing power from volunteers over the internet to do the number-crunching needed to simulate protein folding or scan deep space for extraterrestrial life. SETI@Home was the first such large-scale distributed computing platform. This platform, now the Berkeley Open Infrastructure for Network Computing (BOINC), is today used for many other distributed computing projects such as attempts to search for gravitational waves, do climate modeling, and simulate particle collisions in the Large Hadron Collider.

Folding@Home, a project started by the Pande group at Stanford to use distributed computing to study protein folding uses a similar approach, albeit with different underlying software (is it any wonder that a Stanford group doesn’t use Berkeley’s distributed computing platform?! ) . It has probably been the most successful distributed computing approach to date, and, as a testament to the power of distributed computing, has become known as the first computing system to break the petaFLOPS barrier – e.g. capable of one quadrillion floating point calculations per second! This has enabled the team to do protein-folding simulations on a scale of ~10 micro-seconds.

But, as impressive as the science achieved by distributed computing projects is, what impresses me the most is that projects like Folding@Home and SETI@Home have defined some brilliant new ways to do science:

• Use the internet – It’s a common theme on Bench Press, but with more and more people having faster and faster access to the internet, the potential for distributed computing becomes greater and greater. As Folding@Home demonstrated, such approaches can produce computing systems as powerful (or potentially more powerful) as leading supercomputer systems at a fraction of the cost.
• Mobilize the public – We’ve discussed ways for the scientific community to reach out to the public like using social media and creating interactive applications/tools for the public to use, but efforts like Folding@Home illustrate a way to not only reach out to the public but to get them vested in science. In a world where high school science teachers find it difficult to get teens interested in science, initiatives like Folding@Home have created a system where teams of individuals compete on who can contribute the most to the effort! Instead of simply hoping that the public will continue to fund and listen, why not borrow a page from the many existing cancer-walk-a-thons and make it easy for the public to get involved?
• Leverage new technology – It may not come as a surprise to our readers that a significant amount of the computational power at Folding@Home comes from graphics cards and Playstation 3’s. But, while many “mainstream” supercomputers ignored the new power afforded by these new chip types, Folding@Home developed software so that volunteers could quickly and easily use these powerful chips to boost their Folding@Home scores. The Folding@Home initiative also developed software to take advantage of innovations AMD and Intel included in their chips (new multi-core architectures and special instructions to speed up calculations). Is it any wonder, then, that Sony, NVIDIA, and AMD have all publically announced support for the initiative with their products?

I don’t pretend that every scientific problem is amenable to a distributed computing initiative, but to some extent, I believe that every scientific endeavor has something valuable to learn from the success of Folding@Home and SETI@Home and their brethren. To that end, I sincerely hope to see an open-source distributed computing architecture like BOINC but with:

• Support for new chip technologies – To provide greater value to the scientific effort, the architecture should support new chip technologies like Intel’s SSE extensions, SMP, or stream processing
• Client contribution tracking – To make it easier for volunteers to know how much they’ve contributed and/or have contests on how much they’ve contributed, a simple system to enable users/administrators to track the effort is needed
• Better security – Medical initiatives and volunteer privacy concerns demand that very fine and specialized security controls are necessary. Support for sophisticated encryption and authentication are a must.
• Linkage to social media – This probably seems extraneous, but since distributed computing efforts depend on motivated volunteers actively seeking out new volunteers, a successful architecture needs to make it easy for volunteers to share their progress with their friends whether it be via blog, or social network, or Twitter, or anything.
• Tie-in with new cloud computing systems – Along the theme of cutting costs, it is reasonable to assume that as offerings like Google’s App Engine and Amazon’s EC2 and technologies like MapReduce become better developed, we will see cash-strapped research groups using the power of “Clouds” to hold their computing power – after all, what is distributed/grid computing other than a specific variant of cloud computing (de-localized, pooled computing)? It’s probably necessary, then, for the new distributed computing architecture to more easily link with EC2 or MapReduce or App Engine.

Anyone else have any thoughts?

(Image Credit – picture of the internet) (Image Credit – Folding@Home computing power)