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Kappa Description

Advanced software framework

Kappa is an advanced software framework that makes it easy to bring the best performance to processing data by using relatively inexpensive highly parallel CPU and GPU hardware. Kappa works with your current programming languages and data sources. Kappa is ready to improve your TCO by implementing the production work loads of clusters of hundreds of servers on just a few servers.

The Kappa framework provides practical implementations of massively parallel processing (MPP) using CUDA GPU, OpenMP, and partitioned data flow scheduled processing. It allows processing to be specified using SQL and index component notation for dynamic scaling. The Kappa framework passes (subsets) of data sets between processing kernels and into and out of data sets. The Kappa framework provides Apache Portable Runtime (APR) database driver SQL connections to retrieve data fields from any database source.

For data in a database in star schema format or in an OLTP schema, you can use the Kappa framework for high speed, massively parallel processing of the data. The data is transferred in binary form using an extension of the Apache Portable Runtime parameter specification that also specifies the data structure layout for CUDA or OpenMP data structures. If your data is not in a database, you can use existing C, C++, Perl, or Python libraries within the Kappa framework to access the data.

The Kappa framework provides for:
* Transferring with as few processing steps as possible,
* (optional) primary key fields used to specify record selection for reading and updating,
* dimension and measure field support,
* the ability to normalize nonnumeric dimension discrete fields,
* and the ability to use database fields to split the task for parallel transfer and computation
provide for scalable, efficient, and high speed transfer and processing. These techniques provide for full, efficient usage of database server, bandwidth, and CPU and GPU capacity.

The Kappa framework uses producer/consumer data flow scheduling which maps well to database transactional processing. The data flow scheduling can be indexed and scaled using data from SQL operations or CPU or GPU calculations. GPU kernel launches can be dynamically sized from SQL or other data sources. The data flow scheduling is declarative using SQL and index component (tensor) notation. The data flow scheduling can be specified once and then automatically sized by the data contained in the database data set. The data flow scheduling can be compiled into shared libraries for distribution.

The Kappa framework currently has bindings for C/C++, SQL, Perl, Python, Ruby, Lua, and .Net. Java (and other language) bindings are available but not yet tested. Languages with bindings can be mixed together within a single processing task to implement different steps of the processing.

Currently, the KappaAPRDBD driver is available for use of the Apache Portable Runtime database drivers. Apache Portable Runtime database drivers mean that the drivers are accessible, supported by a community, and can work with the data sources you wish to use. The APR pgsql dbd driver is robust, performs well, and is available for most platforms.

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