The current shared-memory based parallel matrix solutions(R-ScaLAPACK, ScaLAPACK, LAPACK, MPI, ... , etc) provides a scalable and high performance matrix operations, however, matrix resources can't be scalable.
Using HDFS space, we are able to store large matrix. Using iterative algorithms on a parallel processing platform like MapReduce, we should be able to implement World's Largest Matrix Operating System. On 1/22/08, Ted Dunning <[EMAIL PROTECTED]> wrote: > > I should be able to talk about recommendation systems in hadoop. > > > On 1/16/08 9:48 AM, "Ajay Anand" <[EMAIL PROTECTED]> wrote: > > > Yahoo plans to host a summit / workshop on Apache Hadoop at our > > Sunnyvale campus on March 25th. Given the interest we are seeing from > > developers in a broad range of organizations, this seems like a good > > time to get together and brief each other on the progress that is being > > made. > > > > > > > > We would like to cover topics in the areas of extensions being developed > > for Hadoop, innovative applications being built and deployed on Hadoop, > > and future extensions to the platform. Some of the speakers who have > > already committed to present are from organizations such as IBM, Intel, > > Carnegie Mellon University, UC Berkeley, Facebook and Yahoo!, and we are > > actively recruiting other leaders in the space. > > > > > > > > If you have an innovative application you would like to talk about, > > please let us know. Although there are limitations on the amount of time > > we have, we would love to hear from you. You can contact me at > > [EMAIL PROTECTED] > > > > > > > > Thanks and looking forward to hearing about your cool apps, > > > > Ajay > > > > > > > > -- B. Regards, Edward yoon @ NHN, corp.