KMR: Main

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Welcome to KMR#

This is KMR, a high-performance map-reduce library. KMR-1.0 is available on the K computer since 2013-04-26. KMR works on ordinary clusters as well.

Latest release is KMR-1.1 (2013-09-20)..

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, such as K and Fujitsu FX10. On these platforms, KMR provides utilities other than the map-reduce operations which address issues such as accessing very large file-systems.

KMR is designed to work on-memory and exploits 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.

Project Site#

Project Overview and other Activities of the Team at RIKEN:
- http://mt.aics.riken.jp

Documents#

Overview and API Document:
- http://mt.aics.riken.jp/kmr/docs/kmr-1.1/html/index.html
- It is a Doxgen generated document, included in the installation.

//* Overview and API Document (Newer, corrected, for the next release): //** https://mt.aics.riken.jp/kmr/docs/next/html/index.html

//** Documentation in the distribution is late, and it is placed here for late breaking.

Downloading#

Source Code Download:
- Download
- KMR source is available with LGPL-2.1.

Issue Reporting#

KMR Issue Tracker:
- http://mt.aics.riken.jp/jtrac/
- Please make a new user by the login-page to report a new issue.

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.
It describes an overview and optimisations used in KMR.

DISCLAIMER#

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

Acknowledgment#

KMR is a product of RIKEN AICS. Part of the results is obtained by using the K computer at RIKEN AICS.