Hama is a proposed distributed matrix implementation system that is based on using map-reduce to implement basic matrix operations and uses hbase to store matrices. Getting useful performance out of this substrate for dense matrix operations is likely to be fairly challenging due to I/O costs. For sparse operations that exceed memory size, it may be more attractive.
Hama has been around for nearly two years. So far, it appears that there is an implementation of matrix multiply and add. Performance numbers are underwhelming for dense matrices. On sample problem of multiplying 5000 x 5000 random matrices, hama achieves a speed on 8 workstations that is about 1/3 of the speed of R running on a laptop. Performance on sparse matrices may be better. On Fri, May 22, 2009 at 10:13 AM, Edward J. Yoon <edwardy...@apache.org>wrote: > > Is Hama related to Hadoop ? > > Yes, it is. -- Ted Dunning, CTO DeepDyve
