!!! Welcome to KMR

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.

__Latest release is KMR-1.9 (2018-08-27)__.

KMR is a set of high-performance map-reduce operations in the MPI (Message Passing Interface) environment.
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.
KMR provides utilities other than map-reduce operations to address issues such as accessing very large file-systems, on platforms K and Fujitsu FX10.

KMR is designed to work in-memory and exploit large amount of memory available on supercomputers, whereas most map-reduce implementations are designed to work with external (disk-based) operations.
So, data exchanges in KMR occur as message passing instead of remote file operations.
The KMR routines work in bulk-synchronous and the most part of the code is sequential, but the code inside the mapper and reducer are multi-threaded.

!!Documents

* [Overview and API Document|https://riken-rccs.github.io/kmr/]
** It is a Doxgen generated document, included in the installation.

!!Downloading

* [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

* [cluster2013.pdf]: __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.  [IEEE Explore|http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6702663]\\
 It describes an overview and optimizations used in KMR.

* [hpcs2014.pdf]: __Supporting Workflow Management of Scientific Applications by MapReduce Programming Model__. Shinichiro Takizawa, Motohiko Matsuda, and Naoya Maruyama. IPSJ HPCS 2014. (Japanese)\\
 It describes some scientific applications workflow implemented in MapReduce using KMR.

* [bigdata2014.pdf]: __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.  [ACM Digital Library|http://dl.acm.org/citation.cfm?id=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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!!DISCLAIMER

KMR comes with ABSOLUTELY NO WARRANTY.
This wiki also comes with ABSOLUTELY NO WARRANTY.
Contents are liable to change.

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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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