Awesome news! Congratulations :) On Thu, Sep 30, 2010 at 11:18 AM, Edward J. Yoon <[email protected]> wrote: > You can download that paper here > https://blogs.apache.org/hama/entry/hama_in_academic_paper > > And, If you have some feedback about this, just reply here or directly > contact to Mr.Seo. > > ---------- Forwarded message ---------- > From: MAPRED'2010 <[email protected]> > Date: Wed, Sep 22, 2010 at 6:32 AM > Subject: MAPRED'2010 notification for paper 1 : ACCEPTED > To: "Edward J. Yoon" <[email protected]> > > > It is our pleasure to inform you that your paper HAMA: An Efficient > Matrix Computation with the MapReduce Framework has been ACCEPTED for > MAPRED'2010 workshop > at CLOUDCOM'2010. > > Please, use this short time before the camera-ready submission to > improve your papers. > > In particular, please consider comments from the most negative reviews. > > We are looking forward to meeting you at the workshop. > > Further details about final submission will come soon. > > > ---------------------------- REVIEW 1 -------------------------- > PAPER: 1 > TITLE: HAMA: An Efficient Matrix Computation with the MapReduce Framework > > OVERALL RATING: 2 (accept) > REVIEWER'S CONFIDENCE: 4 (expert) > > The paper gives an overview of how Hama represents sparse and dense > matrices with column-storage Hbase and performs matrix computations > with mapreduce (multiplication and solving linear systems). Numerical > results with HAMA (using 2 different mapreduce implementations) are > compared with MPI. > > The paper is well-written, but a bit light in detail, e.g. could gain > from providing a concrete example of the matrix representation (both > for sparse and dense) and more description of each field. More > background and explanation for the map() and reduce() methods > presented could also improve the paper (e.g. cancelled alternatives). > > Regarding related work there seems to be a few missing publication > references, e.g. > a) Pregel: a system for large-scale graph processing > b) Distributed non-negative matrix factorization for dyadic data > analysis on mapreduce > > > > ---------------------------- REVIEW 2 -------------------------- > PAPER: 1 > TITLE: HAMA: An Efficient Matrix Computation with the MapReduce Framework > > OVERALL RATING: 1 (weak accept) > REVIEWER'S CONFIDENCE: 3 (high) > > This paper proposes a distributed framework designed for scientific > applications, which provides important primitives > such as matrix and graph computations. This framework, called HAMA, is > based on a layered architecture that makes > use of several computation engines, among which the MapReduce > framework for matrix computation tasks. > As a case study, the paper focuses on matrix multiplication and > solving linear equation systems. The HAMA approach > (built on top of MapReduce) is evaluated on 16 nodes and compared to > the MPI version of the same algorithms. > > The paper is well organized and the matrix computation primitives are > clearly described. However, the authors could > also specify what other primitives are provided by HAMA, as it is not > clear whether the framework supports only those > presented in the case study or it implements a wider range of matrix > computations. > Moreover, it is worth comparing the scalability of the HAMA approach > to the MPI implementation with respect to the > number of nodes used for the computation, not only as a function of > the size of the problem, as shown in the > experiments. > The paper does not include a related work section to compare the HAMA > framework to existing approaches that expose > computation primitives and it does not discuss the performance gain of > using the HAMA framework for scientific > applications. > > > > > > -- > Best Regards, Edward J. Yoon > [email protected] > http://blog.udanax.org >
-- Filipe David Manana, [email protected], [email protected] "Reasonable men adapt themselves to the world. Unreasonable men adapt the world to themselves. That's why all progress depends on unreasonable men."
