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From Filipe David Manana <fdman...@apache.org>
Subject Re: Our paper has been accepted as a workshop paper at IEEE CLOUDCOM'2010
Date Thu, 30 Sep 2010 15:00:35 GMT
Awesome news!
Congratulations :)

On Thu, Sep 30, 2010 at 11:18 AM, Edward J. Yoon <edwardyoon@apache.org> 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 <mapred2010@easychair.org>
> Date: Wed, Sep 22, 2010 at 6:32 AM
> Subject: MAPRED'2010 notification for paper 1 : ACCEPTED
> To: "Edward J. Yoon" <edwardyoon@apache.org>
>
>
> 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
> edwardyoon@apache.org
> http://blog.udanax.org
>



-- 
Filipe David Manana,
fdmanana@gmail.com, fdmanana@apache.org

"Reasonable men adapt themselves to the world.
 Unreasonable men adapt the world to themselves.
 That's why all progress depends on unreasonable men."

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