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At line 1 changed one line
!!! Welcome to KMR (K Map-Reduce)
!!! Welcome to KMR
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This is KMR (K Map-Reduce), a high-performance map-reduce library.
KMR-1.0 is available on K Computer in "/opt/aics/kmr" now (2013-04-26).
This is KMR, a high-performance map-reduce library.
KMR-1.0 is available since 2013-04-26.
KMR works on ordinary clusters as well as large-scale supercomputers.
KMR source code is available under the BSD license.
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__Latest release is KMR-1.10 (2018-11-16)__.
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Its main target is large-scale computers with thousands nodes, K and Fujitsu FX10.
On those platforms, KMR provides utilities for the map-reduce operations to address
issues such as accessing large file-systems.
But, KMR works on clusters as well.
It makes programming for data-processing much easier by hiding low-level details of message passing.
Its main targets are large-scale supercomputers with thousands of compute nodes.
%%strike KMR provides utilities other than map-reduce operations to address issues such as accessing very large file-systems, on platforms K and Fujitsu FX10/%.
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KMR assumes large amount of memory and designed to work on-memory, whereas most map-reduce implementations are designed to work with external (disk-based) operations.
KMR is designed to work in-memory and to exploit large amount of memory available on supercomputers, whereas most map-reduce implementations are designed to work with external (disk-based) operations.
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* Project Overview and other Activities of the Team:
** [http://mt.aics.riken.jp]
!!Documents
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* Overview and API Document:
** [http://mt65.aics.riken.jp/kmrdoc/kmr-1.0/html/index.html]]
* [Overview and API Document|https://riken-rccs.github.io/kmr/]
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*Overview and API Document (Newer, corrected, maybe for next release):
** [http://mt65.aics.riken.jp/kmrdoc/newest/html/index.html]]
** Documentation is late, and it is late breaking.
* KMR Issue Tracker:
** [https://mt65.aics.riken.jp/jtrac/]
** Please make a new user by the login-page to report a new issue.
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* Download:
** [https://mt65.aics.riken.jp/kmr/Wiki.jsp?page=Download]
** KMR source is available with LGPL-2.1.
!!Downloading
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* [Download]
!!Tutorials
* [Tutorial] (in Japanese)
!!Project Site
* KMR in GitHub [https://github.com/riken-rccs]
* Issue reporting [https://github.com/riken-rccs/kmr/issues]
* Other software from RIKEN R-CCS [https://riken-rccs.github.io]
!!Publications
* __K MapReduce: A Scalable Tool for Data-Processing and Search/Ensemble Applications on Large-Scale Supercomputers__. Motohiko Matsuda, Naoya Maruyama, and Shinichiro Takizawa. IEEE Cluster Computing (CLUSTER) 2013. (C) Copyright IEEE. [ieeexplore.ieee.org|https://ieeexplore.ieee.org/document/6702663]\\
It describes an overview and optimizations used in KMR.
* __Supporting Workflow Management of Scientific Applications by MapReduce Programming Model__. Shinichiro Takizawa, Motohiko Matsuda, and Naoya Maruyama. IPSJ HPCS 2014. (in Japanese). [http://id.nii.ac.jp/1001/00096874]\\
It describes some scientific applications workflow implemented in MapReduce using KMR.
* __Evaluation of Asynchronous MPI Communication in Map-Reduce System on the K Computer__. Motohiko Matsuda, Naoya Maruyama, and Shinichiro Takizawa. EuroMPI Workshop 2014. (C) Copyright ACM. [dl.acm.org|https://dl.acm.org/doi/10.1145/2642769.2642800]\\
It compares all-to-all collective communication versus asynchronous communication in shuffling communication, to qualify believed effectiveness of overlapping of communication and computation.
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!!Acknowledgment
KMR is a product of RIKEN R-CCS.
Part of the results is obtained by using K computer at RIKEN R-CCS.
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Are you lost?, for wiki on mt65 try: [https://mt65.aics.riken.jp/wiki/]