FW: Apache Hama in academic paper

HAMA: An Efficient Matrix Computation with the MapReduce Framework

Sangwon Seoyz, Edward J. Yoon, Jae-Hong Kimy, Seongwook Jiny, Jin-Soo Kimx and Seungryoul Maengy
y Computer Science Division, Korea Advanced Institute of Science and Technology (KAIST)
z Computer Science Division, Berlin University of Technology (TU Berlin)
x School of Information and Communication, Sungkyunkwan University, South Korea
User Service Development Center, NHN Corp., South Korea
fswseo, jaehong, swjin, maengg@calab.kaist.ac.kr, edwardyoon@apache.org, jinsookim@skku.edu


Various scientific computations have become so complex, and thus computation tools play an important role. In this paper, we explore the state-of-the-art framework providing high-level matrix computation primitives with MapReduce through the case study approach, and demonstrate these primitives with different computation engines to show the performance and scalability. We believe the opportunity for using MapReduce in scientific computation is even more promising than the success to date in the parallel systems literature.

It is sooner than I'd planned, but nice start. I hope that this project will continue in the future. :)


  1. I can't wait to see it :). Where will it be published?

  2. Hi Felix, It was submitted to "USENIX Workshop on Hot Topics in Parallelism (HotPar '10)"