This is another post from my old blog. There was a comment there too, but since I decided not to import my posts, I will have to leave the comment behind.
For many years people have talked about grid computing. In the scientific computing business, grid computing has been considered by many to be a panacea for various computing problems and there a number of projects are that try and tap into the power of grids. Examples include the world community grid from IBM and United Devices or the efforts from BOINC.
While community grids probably work well for the kinds of projects listed above, I have always wondered how useful they really are for more specific applications, e.g. a drug discovery effort at a pharma company. There are companies that are running grids, but do they really get better results than they might with a cluster. IMO, cluster computing has certain advantages in terms of efficiency that a grid cannot achieve in reality. While Prof. Charles Brooks, via Predictor@home, has demonstrated how multiscale modeling efforts can be applied to grids, I am still somewhat uncertain about their success in a commercial setting. Cluster computing, for now, would be my preference if I was an IT manager. Certainly there are applications, such as lower-priority, long term projects where a grid solution would be optimal (e.g. a routine, version controlled genome annotation project) as it can keep humming along in the background when resources are found within an organization. On the other hand, for a mission critical project (e.g. virtual screening for a specific kinase target), I would prefer to deploy a cluster, i.e. resources that I can control.
What do others think?
Further Reading
Sun’s Grid Flop
Cluster Computing
Clusters better than grids
One Trackback
[...] Power to the people. In one of Joe’s posts, he talks about “making more power open to wider groups of people”. I cannot agree more. We need to be in a situation where the average person who wants a specific task done should not be hardware limited (within reason). This includes the scientist who wants to visualize complex scientific data as well as the researcher who wants to run massive MD simulations. I don’t think everyone looks at the technical specifications of the hardware these days. People look at form factors, ease of use, etc. Perhaps the user doesn’t care where the computation is happening as long as they can run it and look at the results. All these needs point to two areas that I have actually criticized in the past, accelerators and grid computing. I believe that at this point in time both have serious issues with commercialization and acceptance, or perhaps users aren’t quite pushing their current systems in a way that makes them wish they needed these technologies. I also suspect that there is a level of awareness, or lack thereof, that plays a part in adoption as well. [...]