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
